DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/28/2025 and 01/26/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o). Correction of the following is required:
In claim 16, line 1, “computer-readable storage medium” lacks proper antecedent basis support in the specification.
Claim Objections
Claims 8 and 15 are objected to because of the following informalities:
In claim 8, line 1, “The method of claim 7, assigning weights” should read “The method of claim 7, wherein assigning weights”.
In claim 15, lines 4-5, “the data processing apparatuses” should read “the one or more data processing apparatuses” to properly refer to “one or more data processing apparatuses” in line 2.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation “the outcome 0” in line 8. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the outcome 0” has been interpreted as “an outcome 0”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim could be considered signals per se.
Independent claim 16 recites “computer-readable storage medium.” The broadest reasonable interpretation of a claim that recites "computer-readable storage medium," in view of the present specification, covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer-readable storage medium, particularly when the specification is silent. See MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. § 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009; p. 2. 1351 Off. Gaz. Pat. Off. 212 (2010). Under broadest reasonable interpretation, "computer-readable storage medium" recited in claim 16 encompasses a transitory, propagating signal, which is not a process, machine, manufacture, or composition of matter. Nuijten, 500 F.3d at 1357. The claim "covers material not found in any of the four statutory categories [and thus] falls outside the plainly expressed scope of § 101." Id. at 1354. A recommended amendment is to recite “non-transitory computer-readable storage medium” (emphasis added).
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because, under its broadest reasonable interpretation in light of the specification, the claim is directed to software per se.
Regarding claim 17, the claim is directed to a “computer program product comprising instructions”. It is not directed to the medium on which the program is stored, nor does the specification provide any indication that the “computer program product” is anything other than software.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values”
“generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes”
“assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph”
“executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass classifying measurement outcomes using respective in-phase and quadrature values (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use respective in-phase and quadrature values to classify measurement outcomes); generating a detector graph of nodes and edges that labels detection events that occur in the classified measurement outcomes (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a detector graph that labels detection events in the classified measurement outcomes); assigning weights to the detector graph edges using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a weighted detector graph by assigning weights to the edges of the detector graph using posterior probability distributions); and executing a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“computer”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“using the in-phase and quadrature values to compute probability density functions for the classified measurement outcomes, optionally wherein computing the probability density functions for the measurement outcomes comprises using kernel density estimation techniques”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass computing probability density functions for the classified measurement outcomes using the in-phase and quadrature values and using kernel density estimation techniques (corresponds to mathematical calculations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“computing a conditional probability that the measurement operation produces a first outcome given a respective in-phase and quadrature value assuming a uniform prior probability distribution for the measurement operation”
“in response to determining that the conditional probability is greater than or equal to a predetermined threshold, classifying the measurement outcome as the first outcome”
“in response to determining that the conditional probability is less than the predetermined threshold, classifying the measurement outcome as a second outcome”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass computing a conditional probability that the measurement operation produces a first outcome given a respective in-phase and quadrature value assuming a uniform prior probability distribution for the measurement operation (corresponds to mathematical calculations); classifying the measurement outcomes as the first outcome in response to the conditional probability being greater than or equal to a predetermined threshold (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can in response to the conditional probability being greater than or equal to a predetermined threshold, classify the measurement outcomes as the first outcome); and classifying the measurement outcomes as the second outcome in response to the conditional probability being less than a predetermined threshold (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can in response to the conditional probability being less than a predetermined threshold, classify the measurement outcomes as the second outcome).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein computing a conditional probability that the measurement operation produces a first outcome given a respective in-phase and quadrature value assuming a uniform prior probability distribution comprises computing:
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where Pr(1|obs) represents the conditional probability that the measurement operation produces outcome 1 given an in-phase and quadrature value obs, PDF1(obs) represents a value of a probability density function PDF1 for the outcome 1 given the value obs, and PDF0(obs) represents a value of a probability density function PDF0 for the outcome 0 given the value obs”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass using the given equation to compute a conditional probability that the measurement operation produces a first outcome given a respective in-phase and quadrature value assuming a uniform prior probability distribution (corresponds to mathematical calculations and formulas or equations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 3 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“computing a conditional probability that the measurement operation produces outcome i given a respective in-phase and quadrature value assuming a prior probability distribution for the measurement operation”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass computing a conditional probability that the measurement operation produces outcome i given a respective in-phase and quadrature value assuming a prior probability distribution (corresponds to mathematical calculations).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein computing a conditional probability that the measurement operation produces outcome i given a respective in-phase and quadrature value assuming a uniform prior probability distribution comprises computing:
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where Pr(i|obs) represents the conditional probability that the measurement operation produces outcome i given an in-phase and quadrature value obs, PDFk(obs) represents a value of a probability density function PDFk for outcome k given the value obs, and the index j represents all possible measurement outcomes”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass using the given equation to compute a conditional probability that the measurement operation produces outcome i given a respective in-phase and quadrature value assuming a prior probability distribution (corresponds to mathematical calculations and equations or formulas).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 5 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 5 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein generating the detector graph comprises assigning the edges initial weights using component-level benchmarks without measurement error”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating a detector graph of nodes and edges that labels detection events that occur in the classified measurement outcomes by assigning the edges initial weights using component-level benchmarks without measurement error (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a detector graph that labels detection events in the classified measurement outcomes by assigning the edges initial weights using component-level benchmarks without measurement error).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes comprises updating the initial weights to include values of the posterior probability distributions”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass assigning weights to the edges of the detector graph using posterior probability distributions by updating the initial weights to include values of the posterior probability distributions (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can update the initial weights to include values of the posterior probability distributions in order to assign weights to the edges of the detector graph).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 7 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 7 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the weighted detector graph is equivalent to a detector graph generated with measurement error”
As drafted, is part of the abstract idea of claim 1 of generating a weighted detector graph. The limitation of claim 9 further limits the limitation of claim 1 by further defining what the weighted detector graph comprises. The above limitation in the context of this claim encompasses assigning weights to the detector graph edges using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph that is equivalent to a detector graph generated with measurement error (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a weighted detector graph equivalent to a detector graph generated with measurement error by assigning weights to the edges of the detector graph using posterior probability distributions). The limitation:
“wherein individual in-phase and quadrature values are used to determine measurement error probabilities”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass determining measurement error probabilities using individual in-phase and quadrature values (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use individual in-phase and quadrature values to determine measurement error probabilities).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: Please see the analysis of claim 1. The limitations of claim 10 are only additional elements to the abstract ideas of claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“wherein the in-phase and quadrature values are obtained from a quantum computing device that implements the quantum error correction code and performs the quantum computation”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“wherein the quantum computing device transmits 4 additional bits of in-phase and quadrature values per measurement operation”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer and quantum computing device for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” and “… transmits …” limitations are insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph comprises updating weights of edges that are adjacent to detection events”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass assigning weights to the edges of the detector graph using posterior probability distributions by updating weights of edges that are adjacent to detection events (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can update weights of edges that are adjacent to detection events in order to assign weights to the edges of the detector graph).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 12,
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 12 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph comprises down-weighting weights of edges that are adjacent to detection events and that include leakage measurements”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass assigning weights to the edges of the detector graph using posterior probability distributions by down-weighting weights of edges that are adjacent to detection events include leakage measurements (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can down-weight weights of edges that are adjacent to detection events include leakage measurements in order to assign weights to the edges of the detector graph).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 13,
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein in-phase and quadrature values for a respective measurement operation are clustered into a first cluster that represents a 0 measurement outcome of the measurement operation, a 1 measurement outcome of the measurement operation, and a 2 measurement outcome of the measurement operation, wherein the 2 measurement outcome indicates leakage”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass clustering in-phase and quadrature values for a respective measurement operation into a first cluster that represents a 0 measurement outcome of the measurement operation, a 1 measurement outcome of the measurement operation, and a 2 measurement outcome of the measurement operation that indicates leakage (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can cluster in-phase and quadrature values for a respective measurement operation into a first cluster representing 0, 1, and 2 measurement outcomes of the measurement operations, the 2 measurement outcome indicating leakage).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 14,
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“using leaked state discrimination to implement a leakage-aware reweighting strategy”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass using leaked state discrimination to implement a leakage-aware reweighting strategy (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can implement a leakage-aware reweighting strategy using leaked state discrimination).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The recitation of additional elements in claim 1 of a generic computer, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “obtaining …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic computer for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 15,
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to a system, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values”
“generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes”
“assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph”
“executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass classifying measurement outcomes using respective in-phase and quadrature values (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use respective in-phase and quadrature values to classify measurement outcomes); generating a detector graph of nodes and edges that labels detection events that occur in the classified measurement outcomes (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a detector graph that labels detection events in the classified measurement outcomes); assigning weights to the detector graph edges using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a weighted detector graph by assigning weights to the edges of the detector graph using posterior probability distributions); and executing a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“one or more data processing apparatuses”
“non-transitory computer readable storage media in data communication with the one or more data processing apparatuses and storing instructions that, when executed by the data processing apparatuses, cause the one or more data processing apparatuses to perform operations”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic data processing apparatuses and non-transitory computer readable storage media for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 16,
Claim 16 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16, as noted above, is directed towards non-statutory subject matter as signals per se. However, for purposes of this rejection, it will be assumed that the claim is directed to a non-transitory computer-readable storage medium and that the claim is therefore directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values”
“generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes”
“assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph”
“executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass classifying measurement outcomes using respective in-phase and quadrature values (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use respective in-phase and quadrature values to classify measurement outcomes); generating a detector graph of nodes and edges that labels detection events that occur in the classified measurement outcomes (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a detector graph that labels detection events in the classified measurement outcomes); assigning weights to the detector graph edges using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a weighted detector graph by assigning weights to the edges of the detector graph using posterior probability distributions); and executing a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a processing device”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe a generic processing device for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 17,
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 17, as noted above, is directed towards non-statutory subject matter as software per se. However, for purposes of this rejection, it will be assumed that the claim is directed to a non-transitory medium that stores the computer program product and that the claim is therefore directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values”
“generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes”
“assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph”
“executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass classifying measurement outcomes using respective in-phase and quadrature values (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use respective in-phase and quadrature values to classify measurement outcomes); generating a detector graph of nodes and edges that labels detection events that occur in the classified measurement outcomes (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a detector graph that labels detection events in the classified measurement outcomes); assigning weights to the detector graph edges using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a weighted detector graph by assigning weights to the edges of the detector graph using posterior probability distributions); and executing a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can perform a decoding process on the weighted detector graph to compute a decoding output that predicts an occurrence of errors in the quantum computation).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“one or more processing devices”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic processing devices for applying the abstract ideas) or insignificant extra-solution activity (i.e. obtaining/receiving data). Furthermore, the “obtaining …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5, and 7-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jurcevic et al. (US 12,020,122 B1) in view of Sundaresan et al. ("Demonstrating multi-round subsystem quantum error correction using matching and maximum likelihood decoders").
Regarding Claim 1,
Jurcevic et al. teaches a computer implemented method (Col. 4, lines 60-63: "In some embodiments of the present disclosure, the present disclosure comprises a method, system and computer program product for mitigating errors in measurements from a quantum system" teaches a computer implemented method) comprising:
obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation (Col. 11, line 49 - Col. 12, line 13: "In one embodiment, the performance of quantum circuit 109 is simulated via a simulator 203 ... in which the results of such a simulation provide the state of the qubit from the execution of quantum circuit 109 ... the measurement results of the qubits states correspond to sets of two signals in quadrature. A pair of signals is said to be in “quadrature” when they differ in phase by 90 degrees. In one embodiment, the measurement results discussed above correspond to the I (in-phase) and Q (quadrature) time integrated voltage signals ... evaluator engine 202 accepts the measurement results that fall within a region of trust 301 (e.g., region of trust 301A) and rejects those measurement results that fall outside the regions of trust 301 ... By utilizing such regions of trust 301, errors in the measurement results from a quantum system, such as measurement errors of the quantum states read from the execution of quantum circuits 109, are mitigated" teaches obtaining in-phase and quadrature values measurement operations in a quantum error correction code for a quantum computation).
Jurcevic et al. does not appear to explicitly teach classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.
However, Sundaresan et al. teaches classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values (Page 11, third paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid" teaches classifying measurement outcomes based on the in-phase and quadrature values);
generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches generating a decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes, the decoding hypergraph (detector graph) comprising vertices (nodes) and hyperedges (edges));
assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (Page 12, second-fourth paragraphs: "For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches assigning weights to the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph)); and
executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation (Page 12, first-fifth paragraphs: "Soft-information decoding ... Here we attempt this strategy with the matching decoder and find small improvements in our logical error rates per round. Let us first describe how the soft information decoding works. For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment ... Performing minimum-weight perfect matching with these modified edge weights on leakage post-selected data gives the logical error rates" teaches executing a decoding process on the weighted decoding graph (weighted detector graph) to compute decoding outputs to predict error occurrences in the quantum computation).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 2,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches further comprising using the in-phase and quadrature values to compute probability density functions for the classified measurement outcomes, optionally wherein computing the probability density functions for the measurement outcomes comprises using kernel density estimation techniques (Page 11, second paragraph - Page 12, third paragraph: "We instead apply clustering methods to our calibration data using a Gaussian Mixture Model (GMM) with three clusters, each cluster with an independent diagonal covariance matrix. The diagonal entries of the covariance matrices can be used to extract the standard deviations of the distribution for each qubit state ... For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout" teaches in-phase and quadrature values used for classifying measurement outcomes and for computing posterior probability distributions of the classified measurement outcomes).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising using the in-phase and quadrature values to compute probability density functions for the classified measurement outcomes, optionally wherein computing the probability density functions for the measurement outcomes comprises using kernel density estimation techniques as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 3,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values comprises, for each measurement operation: computing a conditional probability that the measurement operation produces a first outcome given a respective in-phase and quadrature value assuming a uniform prior probability distribution for the measurement operation (Page 11, third paragraph - Page 12, fourth paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches in-phase and quadrature values used for classifying measurement outcomes are used for determining measurement error probabilities in the decoding graph (detector graph) including computing P[i|M] (conditional probability that the measurement operation M produces a first outcome)); and
in response to determining that the conditional probability is greater than or equal to a predetermined threshold, classifying the measurement outcome as the first outcome (Page 11, third paragraph - Page 12, fourth paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches in-phase and quadrature values used for classifying measurement outcomes are used for determining measurement error probabilities in the decoding graph (detector graph) including computing P[i|M] (conditional probability that the measurement operation M produces a first outcome), wherein the measurement outcome is classified |1⟩ (first outcome) when the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive (e.g. greater than a predetermined threshold)); or
in response to determining that the conditional probability is less than the predetermined threshold, classifying the measurement outcome as a second outcome (Page 11, third paragraph - Page 12, fourth paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches in-phase and quadrature values used for classifying measurement outcomes are used for determining measurement error probabilities in the decoding graph (detector graph) including computing P[i|M] (conditional probability that the measurement operation M produces a first outcome), wherein the measurement outcome is classified |0⟩ (second outcome) when the integrated outcome is within the |0⟩-state region and the I-quadrature is negative (e.g. less than the predetermined threshold)).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values comprises, for each measurement operation: computing a conditional probability that the measurement operation produces a first outcome given a respective in-phase and quadrature value assuming a uniform prior probability distribution for the measurement operation; and in response to determining that the conditional probability is greater than or equal to a predetermined threshold, classifying the measurement outcome as the first outcome; or in response to determining that the conditional probability is less than the predetermined threshold, classifying the measurement outcome as a second outcome as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 5,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values comprises, for each measurement operation: computing a conditional probability that the measurement operation produces outcome i given a respective in-phase and quadrature value assuming a prior probability distribution for the measurement operation (Page 11, third paragraph - Page 12, fourth paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches in-phase and quadrature values used for classifying measurement outcomes are used for determining measurement error probabilities in the decoding graph (detector graph) including computing P[i|M] (conditional probability that the measurement operation M produces outcome i)).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values comprises, for each measurement operation: computing a conditional probability that the measurement operation produces outcome i given a respective in-phase and quadrature value assuming a prior probability distribution for the measurement operation as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 7,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein generating the detector graph comprises assigning the edges initial weights using component-level benchmarks without measurement error (Page 4, third-fourth paragraphs: "from the decoding hypergraph we keep nodes VZ corresponding to (the difference of subsequent) Z-stabilizer measurements and edges (i.e. hyperedges with size two) between them. Additionally, a boundary vertex b is created, and size-one hyperedges of the form {v} with v ∈ VZ, are represented by including edges {v, b}. All edges in the X-error graph inherit probabilities and logical labels from their corresponding hyperedges ... A perfect matching algorithm takes a graph with weighted edges and an even-sized set of highlighted nodes, and returns a set of edges in the graph that connects all highlighted nodes in pairs and has minimum total weight among all such edge sets ... edge weights are either chosen to all be one (uniform method) or set as we = log((1-pe)/pe), where pe is the edge probability (analytic method). The latter choice means that the total weight of an edge set is equal to the log-likelihood of that set, and minimum weight perfect matching tries to maximize this likelihood over the edges in the graph" teaches the edges of the decoding hypergraph (detector graph) being assigned weights (initial weights) based on measurements and probabilities (component-level benchmarks) without measurement error).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein generating the detector graph comprises assigning the edges initial weights using component-level benchmarks without measurement error as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 8,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 7.
In addition, Sundaresan et al. further teaches assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes comprises updating the initial weights to include values of the posterior probability distributions (Page 12, second-fourth paragraphs: "For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches modifying (updating) assigned weights of the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph)).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes comprises updating the initial weights to include values of the posterior probability distributions as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 9,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein the weighted detector graph is equivalent to a detector graph generated with measurement error, wherein individual in-phase and quadrature values are used to determine measurement error probabilities (Page 11, third paragraph - Page 12, fourth paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid. This classification method is applied to every qubit after every measurement and the experimental runs in which any qubit is measured as |2⟩ is discarded. Figure 5f shows the readout outcomes of QF12 after the last initialization measurement. We only discard uncorrectable errors (|2⟩ state) and retain experimental shots in which a qubit is in the |1⟩ state after initialization, as that should be a correctable error by the code ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches in-phase and quadrature values used for classifying measurement outcomes are used for determining measurement error probabilities in the decoding graph (detector graph)).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein the weighted detector graph is equivalent to a detector graph generated with measurement error, wherein individual in-phase and quadrature values are used to determine measurement error probabilities as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 10,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Jurcevic et al. further teaches wherein the in-phase and quadrature values are obtained from a quantum computing device that implements the quantum error correction code and performs the quantum computation, wherein the quantum computing device transmits 4 additional bits of in-phase and quadrature values per measurement operation (Col. 11, line 49 - Col. 12, line 13: "In one embodiment, the performance of quantum circuit 109 is simulated via a simulator 203 ... in which the results of such a simulation provide the state of the qubit from the execution of quantum circuit 109 ... the measurement results of the qubits states correspond to sets of two signals in quadrature. A pair of signals is said to be in “quadrature” when they differ in phase by 90 degrees. In one embodiment, the measurement results discussed above correspond to the I (in-phase) and Q (quadrature) time integrated voltage signals ... evaluator engine 202 accepts the measurement results that fall within a region of trust 301 (e.g., region of trust 301A) and rejects those measurement results that fall outside the regions of trust 301 ... By utilizing such regions of trust 301, errors in the measurement results from a quantum system, such as measurement errors of the quantum states read from the execution of quantum circuits 109, are mitigated" teaches obtaining in-phase and quadrature values measurement operations in a quantum error correction code for a quantum computation from a quantum circuit (quantum computing device). Col. 3, line 66 - Col. 4, line 2: "If such quantum state measurements are well calibrated, such qubit states may form two clouds (one for the quantum state of 0 and one for the quantum state of 1) such as in an IQ (in-phase and quadrature) plane" teaches that the in-phase and quadrature values are obtained from a quantum computing device based on qubits indicating quantum states associated with the in-phase and quadrature values for the measurements (e.g. 4 additional bits of in-phase and quadrature values to indicate the quantum states can be obtained per measurement operation)).
Regarding Claim 11,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph comprises updating weights of edges that are adjacent to detection events (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches the decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes comprising vertices (nodes) corresponding to error-sensitive events (detection events) and adjacent hyperedges (edges adjacent to detection events). Page 12, second-fourth paragraphs: "For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches modifying (updating) assigned weights of the edges of the decoding graph (detector graph) (e.g. of the edges adjacent to detection events) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph)).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph comprises updating weights of edges that are adjacent to detection events as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 12,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph comprises down-weighting weights of edges that are adjacent to detection events and that include leakage measurements (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches the decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes comprising vertices (nodes) corresponding to error-sensitive events (detection events) and adjacent hyperedges (edges adjacent to detection events). Page 11, second paragraph - Page 12, fifth paragraph: "We post-select all our results to remove detected leakage events in any of the qubits in our system ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment ... Performing minimum-weight perfect matching with these modified edge weights on leakage post-selected data gives the logical error rates" teaches assigning weights to the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph) wherein error-sensitive events (detection events) that include leakage measurements are removed (e.g. adjacent edges to the detection events are down weighted)).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph comprises down-weighting weights of edges that are adjacent to detection events and that include leakage measurements as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 13,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches wherein in-phase and quadrature values for a respective measurement operation are clustered into a first cluster that represents a 0 measurement outcome of the measurement operation, a 1 measurement outcome of the measurement operation, and a 2 measurement outcome of the measurement operation, wherein the 2 measurement outcome indicates leakage (Page 11, third paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid" teaches classifying measurement outcomes based on the in-phase and quadrature values, the measurement outcomes being clustered into a 0 measurement outcome of the measurement operation, a 1 measurement outcome of the measurement operation, and a 2 measurement outcome of the measurement operation. Page 10, first paragraph: "Figure 5b shows the measurement leakage probability,
p
l
e
a
k
m
e
a
s
, where the qubit leaks to the |2⟩ state per measurement" teaches that the |2⟩-state measurement outcome indicates leakage).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate wherein in-phase and quadrature values for a respective measurement operation are clustered into a first cluster that represents a 0 measurement outcome of the measurement operation, a 1 measurement outcome of the measurement operation, and a 2 measurement outcome of the measurement operation, wherein the 2 measurement outcome indicates leakage as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 14,
Jurcevic et al. in view of Sundaresan et al. teaches the method of claim 1.
In addition, Sundaresan et al. further teaches further comprising using leaked state discrimination to implement a leakage-aware reweighting strategy (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches the decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes comprising vertices (nodes) corresponding to error-sensitive events (detection events) and adjacent hyperedges (edges adjacent to detection events). Page 11, second paragraph - Page 12, fifth paragraph: "We post-select all our results to remove detected leakage events in any of the qubits in our system ... For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment ... Performing minimum-weight perfect matching with these modified edge weights on leakage post-selected data gives the logical error rates" teaches assigning weights to the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes including a leakage state to generate a weighted decoding graph (weighted detector graph) wherein error-sensitive events (detection events) that include leakage measurements are removed (e.g. leakage-aware reweighting strategy). Page 10, first paragraph: "Figure 5b shows the measurement leakage probability,
p
l
e
a
k
m
e
a
s
, where the qubit leaks to the |2⟩ state per measurement" teaches that the |2⟩-state measurement outcome indicates leakage).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate further comprising using leaked state discrimination to implement a leakage-aware reweighting strategy as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 15,
Jurcevic et al. teaches a system comprising: one or more data processing apparatuses; and non-transitory computer readable storage media in data communication with the one or more data processing apparatuses and storing instructions that, when executed by the data processing apparatuses, cause the one or more data processing apparatuses to perform operations (Col. 4, lines 60-63: "In some embodiments of the present disclosure, the present disclosure comprises a method, system and computer program product for mitigating errors in measurements from a quantum system" teaches a system and method for performing the embodiments. Fig. 4; Col. 13, line 8 - Col. 14, line 8: "Computing environment 400 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as mitigating measurement errors from a quantum system without requiring a large number of shots. In addition to block 401, computing environment 400 includes, for example, classical computer 102 ... classical computer 102 includes processor set 406 (including processing circuitry 407 and cache 408) ... Computer readable program instructions are typically loaded onto classical computer 102 to cause a series of operational steps to be performed by processor set 406 of classical computer 102 and thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 408 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 406 to control and direct performance of the inventive methods" teaches the system comprising processor set 406 (data processing apparatuses) and computer readable storage media in communication with the data processor set and storing instructions for execution by the processor set to perform the operations) comprising:
obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation (Col. 11, line 49 - Col. 12, line 13: "In one embodiment, the performance of quantum circuit 109 is simulated via a simulator 203 ... in which the results of such a simulation provide the state of the qubit from the execution of quantum circuit 109 ... the measurement results of the qubits states correspond to sets of two signals in quadrature. A pair of signals is said to be in “quadrature” when they differ in phase by 90 degrees. In one embodiment, the measurement results discussed above correspond to the I (in-phase) and Q (quadrature) time integrated voltage signals ... evaluator engine 202 accepts the measurement results that fall within a region of trust 301 (e.g., region of trust 301A) and rejects those measurement results that fall outside the regions of trust 301 ... By utilizing such regions of trust 301, errors in the measurement results from a quantum system, such as measurement errors of the quantum states read from the execution of quantum circuits 109, are mitigated" teaches obtaining in-phase and quadrature values measurement operations in a quantum error correction code for a quantum computation).
Jurcevic et al. does not appear to explicitly teach classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.
However, Sundaresan et al. teaches classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values (Page 11, third paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid" teaches classifying measurement outcomes based on the in-phase and quadrature values);
generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches generating a decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes, the decoding hypergraph (detector graph) comprising vertices (nodes) and hyperedges (edges));
assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (Page 12, second-fourth paragraphs: "For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches assigning weights to the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph)); and
executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation (Page 12, first-fifth paragraphs: "Soft-information decoding ... Here we attempt this strategy with the matching decoder and find small improvements in our logical error rates per round. Let us first describe how the soft information decoding works. For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment ... Performing minimum-weight perfect matching with these modified edge weights on leakage post-selected data gives the logical error rates" teaches executing a decoding process on the weighted decoding graph (weighted detector graph) to compute decoding outputs to predict error occurrences in the quantum computation).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 16,
Jurcevic et al. teaches a computer-readable storage medium comprising instructions stored thereon that are executable by a processing device and upon such execution cause the processing device to perform operations (Col. 4, lines 60-63: "In some embodiments of the present disclosure, the present disclosure comprises a method, system and computer program product for mitigating errors in measurements from a quantum system" teaches a computer program product for performing the embodiments. Col. 12, lines 39-45: "A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim" teaches the computer program product comprising a storage medium (computer-readable storage medium) storing instructions for execution by a computer (processing device) for performing operations) comprising:
obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation (Col. 11, line 49 - Col. 12, line 13: "In one embodiment, the performance of quantum circuit 109 is simulated via a simulator 203 ... in which the results of such a simulation provide the state of the qubit from the execution of quantum circuit 109 ... the measurement results of the qubits states correspond to sets of two signals in quadrature. A pair of signals is said to be in “quadrature” when they differ in phase by 90 degrees. In one embodiment, the measurement results discussed above correspond to the I (in-phase) and Q (quadrature) time integrated voltage signals ... evaluator engine 202 accepts the measurement results that fall within a region of trust 301 (e.g., region of trust 301A) and rejects those measurement results that fall outside the regions of trust 301 ... By utilizing such regions of trust 301, errors in the measurement results from a quantum system, such as measurement errors of the quantum states read from the execution of quantum circuits 109, are mitigated" teaches obtaining in-phase and quadrature values measurement operations in a quantum error correction code for a quantum computation).
Jurcevic et al. does not appear to explicitly teach classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.
However, Sundaresan et al. teaches classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values (Page 11, third paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid" teaches classifying measurement outcomes based on the in-phase and quadrature values);
generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches generating a decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes, the decoding hypergraph (detector graph) comprising vertices (nodes) and hyperedges (edges));
assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (Page 12, second-fourth paragraphs: "For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches assigning weights to the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph)); and
executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation (Page 12, first-fifth paragraphs: "Soft-information decoding ... Here we attempt this strategy with the matching decoder and find small improvements in our logical error rates per round. Let us first describe how the soft information decoding works. For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment ... Performing minimum-weight perfect matching with these modified edge weights on leakage post-selected data gives the logical error rates" teaches executing a decoding process on the weighted decoding graph (weighted detector graph) to compute decoding outputs to predict error occurrences in the quantum computation).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Regarding Claim 17,
Jurcevic et al. teaches a computer program product comprising instructions which, when the program is executed by one or more processing devices, cause the one or more processing devices to carry out operations (Col. 4, lines 60-63: "In some embodiments of the present disclosure, the present disclosure comprises a method, system and computer program product for mitigating errors in measurements from a quantum system" teaches a computer program product for performing the embodiments. Col. 12, lines 39-45: "A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim" teaches the computer program product comprising instructions for execution by a computer (processing device) for performing operations) comprising:
obtaining in-phase and quadrature values for multiple measurement operations in a quantum error correction code for a quantum computation (Col. 11, line 49 - Col. 12, line 13: "In one embodiment, the performance of quantum circuit 109 is simulated via a simulator 203 ... in which the results of such a simulation provide the state of the qubit from the execution of quantum circuit 109 ... the measurement results of the qubits states correspond to sets of two signals in quadrature. A pair of signals is said to be in “quadrature” when they differ in phase by 90 degrees. In one embodiment, the measurement results discussed above correspond to the I (in-phase) and Q (quadrature) time integrated voltage signals ... evaluator engine 202 accepts the measurement results that fall within a region of trust 301 (e.g., region of trust 301A) and rejects those measurement results that fall outside the regions of trust 301 ... By utilizing such regions of trust 301, errors in the measurement results from a quantum system, such as measurement errors of the quantum states read from the execution of quantum circuits 109, are mitigated" teaches obtaining in-phase and quadrature values measurement operations in a quantum error correction code for a quantum computation).
Jurcevic et al. does not appear to explicitly teach classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation.
However, Sundaresan et al. teaches classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values (Page 11, third paragraph: "For any given measurement in any of the qubits, if the integrated outcome is within the |0⟩-state region and the I-quadrature is negative, we classify that outcome as |0⟩. If the integrated outcome is not within the |0⟩-state region or the I-quadrature is positive, if it is within the |1⟩-state region we classify it as |1⟩, and if it is within the |2⟩-state region but not within the |1⟩-state region, we classify it as |2⟩. For all other results, we classify the output according to its closest centroid" teaches classifying measurement outcomes based on the in-phase and quadrature values);
generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes (Page 2, sixth-seventh paragraphs: "In the setting of FT quantum computing, a decoder is an algorithm that takes as input syndrome measurements from an error correcting code and outputs a correction to the qubits or measurement data. In this section we describe two decoding algorithms: perfect matching decoding and maximum likelihood decoding. The decoding hypergraph is a concise description of the information gathered by a FT circuit and made available to a decoding algorithm. It consists of a set of vertices, or error-sensitive events, V, and a set of hyperedges E, which encode the correlations between events caused by errors in the circuit" teaches generating a decoding hypergraph (detector graph) that labels error-sensitive events (detection events) in the measurement outcomes, the decoding hypergraph (detector graph) comprising vertices (nodes) and hyperedges (edges));
assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph (Page 12, second-fourth paragraphs: "For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We can use Bayes’ rule to write P[i|M]=P[M|i]P[i]/P[M], where P[i] and P[M] are a priori probabilities ... the likelihood ratio to L[M]..., a ratio of probabilities that are calculated directly from the experimental readout ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment" teaches assigning weights to the edges of the decoding graph (detector graph) using posterior probability distributions of the classified measurement outcomes to generate a weighted decoding graph (weighted detector graph)); and
executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation (Page 12, first-fifth paragraphs: "Soft-information decoding ... Here we attempt this strategy with the matching decoder and find small improvements in our logical error rates per round. Let us first describe how the soft information decoding works. For each measurement, three probabilities can be calculated based on the |0⟩, |1⟩, |2⟩ classification Gaussians described in Section “Post-selection method”. These probabilities are P[M|i], the probability of measurement M assuming the true qubit state was i = 0, 1, 2 ... We now modify the edge weights we and edge flip probabilities
p
~
e in the decoding graph (the same graph used by the uniform and analytical matching decoders). The first change is that pmeasure in Table 1 is replaced by the appropriate likelihoods L[M]. Note that while pmeasure refers to the average probability a measurement fails, L[M] is different for each of the ... measurements M in a Z-basis (or X-basis) experiment ... Performing minimum-weight perfect matching with these modified edge weights on leakage post-selected data gives the logical error rates" teaches executing a decoding process on the weighted decoding graph (weighted detector graph) to compute decoding outputs to predict error occurrences in the quantum computation).
Jurcevic et al. and Sundaresan et al. are analogous to the claimed invention because they are directed towards quantum error correction.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate classifying measurement outcomes of the multiple measurement operations using respective in-phase and quadrature values; generating a detector graph of nodes and edges, wherein the detector graph labels detection events that occur in the classified measurement outcomes; assigning weights to the edges of the detector graph using posterior probability distributions of the classified measurement outcomes to generate a weighted detector graph; and executing a decoding process on the weighted detector graph to compute a decoding output of the decoding process, wherein the decoding output predicts an occurrence of errors in the quantum computation as taught by Sundaresan et al. to the disclosed invention of Jurcevic et al.
One of ordinary skill in the art would have been motivated to make this modification to provide "the capability of real-time feedback on a superconducting qubit system with a maximum likelihood decoding protocol hitherto unexplored experimentally in order to improve the survivability of logical states" (Sundaresan et al. Page 2, second paragraph).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
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/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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