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 .
Claims 1-20 have been examined.
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 12 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 12 recites the limitation "wherein calibrating the qubit comprises …". There is insufficient antecedent basis for this limitation in the claim. However, claim 11 appears to provide antecedent basis. For the purpose of further examination, the claim will be interpreted as being dependent upon claim 11.
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 20 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 while the claim is directed to a “computer-readable storage medium,” the claim could be broadly interpreted to include non-tangible transitory media. The originally filed specification at ¶ 0084 provides a discussion of a “non-transitory storage medium,” but uses non-limiting language such as “may be implemented.” This portion of the disclosure also includes “program instructions may be encoded on an artificially-generated propagated signal.” These descriptions of a “medium” do not limit the medium to a tangible article, and could be interpreted to include such non-tangible transitory media as wireless transmission media, or an electromagnetic signal. Claims that recite nothing but the physical characteristics of a form of energy, such as a frequency, voltage, or the strength of a magnetic field, define energy or magnetism, per se, and as such are nonstatutory natural phenomena. Moreover, it does not appear that a claim reciting a signal encoded with functional descriptive material falls within any of the categories of patentable subject matter set forth in Sec. 101. First, a claimed signal is clearly not a "process" under § 101 because it is not a series of steps. A claimed signal has no physical structure, does not itself perform any useful, concrete and tangible result and, thus, does not fit within the definition of a machine. A claimed signal is not matter, but a form of energy, and therefore is not a composition of matter. A product is a tangible physical article or object, some form of matter, which a signal is not. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). In contrast, a computer-readable medium (e.g. magnetic or optical disk) claimed as a "non-transitory" medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure’s functionality to be realized, and is thus statutory. See MPEP 2106.03(I).
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, 11-12, 14-15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 20210334689 by Klimov et al. ("Klimov") in view of U.S. Patent Application Publication 20190042973 by Zou et al. ("Zou").
In regard to claim 1, Klimov discloses:
1. A method of operating a quantum computing system, the method comprising: See Klimov, at least Fig. 3, broadly depicting a method.
obtaining characterization data associated with an operating parameter of a qubit in a quantum computing system; Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, …”
implementing an … operation to extract one or more anomalies from the characterization data; and Klimov ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.”
Klimov does not expressly disclose: unsupervised learning. However, this is taught by Zou. See Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
Klimov also discloses:
operating the qubit in the quantum computing system based at least in part on the one or more anomalies. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
In regard to claim 2, Klimov also discloses:
2. The method of claim 1, wherein the one or more anomalies comprise one or more future predicted anomalies. Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, such as … predicted and/or measured relaxation and/or coherence times of the qubits included in the quantum computing device.”
In regard to claim 3, Klimov also discloses:
3. The method of claim 1, wherein the qubit is a frequency tunable qubit, and the operating parameter comprises an operating frequency of the frequency tunable qubit. Klimov, ¶ 0043, “Each qubit may be operated using respective operating frequencies, … The operating frequencies may be chosen before a computation is performed by the quantum computing device.”
In regard to claim 4, Klimov also discloses:
4. The method of claim 1, wherein the characterization data comprises qubit energy relaxation time versus time. Klimov, ¶ 0044, “One proxy for assessing how good a particular operating frequency is for a particular qubit is that qubit's relaxation time (T1) at that frequency. … However, as shown in plot 100, in reality T1 varies sporadically and unpredictably in qubit frequency (and, although not shown in FIG. 1, in time and from qubit to qubit.) due to uncontrollable defects, as shown by the downward spikes 106.”
In regard to claim 5, Klimov also discloses:
5. The method of claim 1, wherein the one or more anomalies comprise one or more two-level-system defects. Klimov, ¶ 0102, “It is understood that the term “qubit” encompasses all quantum systems that may be suitably approximated as a two-level system in the corresponding context.”
In regard to claim 11, Klimov also discloses:
11. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises calibrating the qubit based at least in part on the one or more anomalies. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
In regard to claim 12, Klimov also discloses:
12. The method of claim [11], wherein calibrating the qubit comprises modifying an operating parameter associated with the qubit. Klimov, ¶ 0052 as cited above, e.g. “adjust qubit operation frequency values …”
In regard to claim 14, Klimov also discloses:
14. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises modifying one or more quantum hardware parameters based at least in part on the one or more anomalies. Klimov, ¶ 0050, “The operating state of the quantum device may be defined as the set of qubit operation frequencies, e.g., idling and interaction frequencies, that are used by the quantum device.” Also ¶ 0052 as cited above, e.g. “adjust qubit operation frequency values …”
In regard to claim 15, Klimov does not expressly disclose:
15. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises modifying one or more environmental parameters based at least in part on the one or more anomalies. However, this is taught by Zou, e.g. ¶ 0092, “As quantum devices continue to mature, there is an emerging need to efficiently organize and orchestrate all elements of the control electronics stack so that the quantum physical chip can be manipulated (electrical controls, microwaves, flux) and measured with acceptable precision, allowing quantum experiments and programs to be conducted in a reliable and repeatable manner.” (Note interpretation in view of Applicant’s as-filed specification ¶ 0026). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s environmental parameters with Klimov’s system in order to operate a quantum system in a reliable and repeatable manner as suggested by Zou.
In regard to claim 20, Klimov discloses:
20. A computer-readable storage medium comprising instructions that are executable by a classical or quantum processing device and upon such execution cause the classical or quantum processing device to perform operations comprising: Klimov ¶ 0101, “Implementations of the digital and/or quantum subject matter described in this specification can be implemented as one or more digital and/or quantum computer programs, i.e., one or more modules of digital and/or quantum computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus.”
obtaining characterization data associated with an operating parameter of a qubit in a quantum computing system; Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, …” Also ¶ 0066, e.g. “T1,qXY−1(t) represents relaxation time during the frequency sweep qXY, which depends on fqXY(t).”
implementing an … operation to extract one or more predicted anomalies from the characterization data; and Klimov ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, such as … predicted and/or measured relaxation and/or coherence times of the qubits included in the quantum computing device.” Also ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.”
Klimov does not expressly disclose: unsupervised learning. However, this is taught by Zou. See Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
modifying an operating parameter of the qubit in the quantum computing system based at least in part on the one or more predicted anomalies. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou as applied above, and further in view of U.S. Patent Application Publication 20140372346 by Phillipps et al. ("Phillipps").
In regard to claim 6, Klimov does not expressly disclose:
6. The method of claim 1, wherein the unsupervised learning operation comprises a clustering operation. This is taught by Phillipps, ¶ 0079, “As shown, in one embodiment, the unsupervised learning module 208 includes a clustering module 230.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Phillipps clustering with the learning operations of Zou and Klimov in order to summarize and explain key features of a data set as suggested by Phillipps (see ¶ 0072).
In regard to claim 7, Phillipps also teaches:
7. The method of claim 6, wherein the clustering operation comprises a density-based clustering operation or a spectral-based clustering operation. Phillipps, ¶ 0079, “Non-limiting examples of clustering algorithms include … density-based clustering algorithms, spectral clustering algorithms.”
Claim(s) 8 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou as applied above, and further in view of U.S. Patent Application Publication 11017321 by Mishra et al. ("Mishra").
In regard to claim 8, Klimov does not expressly disclose:
8. The method of claim 1, wherein the method comprises pre-processing the characterization data prior to implementation of the unsupervised learning operation. This is taught by Mishra, col. 18, lines 4-7, “In some implementations, the monitoring device 102 may perform pre-processing on the operating characteristics data 136 and the events 110 prior to extracting features from the operating characteristics data 136 and the events 110.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Mishra’s pre-processing with Klimov’s input data in order to reduce complexity of feature extraction, reduce the memory footprint associated with the operating characteristics data and events, clean up the operating characteristics data and events, format the operating characteristics data and events, or a combination thereof, as suggested by Mishra (col. 18, lines 7-13).
In regard to claim 10, Klimov and Mishra further teach:
10. The method of claim 8, wherein pre-processing the characterization data comprises extracting data points that exceed a defined threshold. Mishra, col. 18, lines 13-23, “For example, the pre-processing may include … removing an entry from the operating characteristics data 136 that is associated with a variance that fails to satisfy a variance threshold, …”
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou and Mishra as applied above, and further in view of U.S. Patent Application Publication 20190057301 by Pantazi et al. ("Pantazi").
In regard to claim 9, Klimov, Zou and Mishra do not expressly teach:
9. The method of claim 8, wherein pre-processing the characterization data comprises inverting the characterization data. However, this is taught by Pantazi, e.g. ¶ 0029, “Data inverter 10 receives the input image data and produces complementary pixel data for each pixel of the image. The complementary pixel data defines a value which is complementary to that of the pixel data for the corresponding pixel.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Pantazi’s data inversion with the pre-processing of Klimov and Mishra in order to provide advantages such as increasing sensibility for correlation detection, leading to improved neural network performance as suggested by Pantazi (see ¶ 0008).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou as applied above, and further in view of “Fluctuations of Energy-Relaxation Times in Superconducting Qubits” by Klimov et al. (“Klimov-NPL”).
In regard to claim 13, Klimov does not expressly disclose:
13. The method of claim 1, wherein operating the qubit in the quantum computing system based at least in part on the one or more anomalies comprises determining one or more of a density, diffusivity, velocity, or an acceleration associated with the one or more anomalies. However, this is taught by Klimov-NPL, p. 4, par. 3, e.g. “To estimate the density of TF defects and to understand the relationship between the telegraphic and diffusive regimes, we run a Markov-Chain Monte Carlo simulation of interacting defect dynamics in a thin film representative of the interfacial dielectrics in our qubit circuit. Since the diffusivity of TLS transitions is expected to depend strongly on TF density, we use it to connect simulation to experiment.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the density and diffusivity of Klimov-NPL with Klimov’s anomalies in order to utilize an estimation of system state as essentially suggested by Klimov-NPL.
Claim(s) 16 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou and U.S. Patent Application Publication 20190165244 by Hertzberg et al. ("Hertzberg").
In regard to claim 16, Klimov discloses:
16. A quantum computing system comprising: a plurality of superconducting qubits, each qubit configured to be operated using an operating frequency, each operating frequency associated with an energy relaxation time; Klimov, ¶ 0043, “Quantum computing devices often include multiple qubits arranged in a two-dimensional grid, where neighboring qubits allowed to interact. Each qubit may be operated using respective operating frequencies, e.g., respective idling and interaction frequencies.” Also ¶ 0044, “One proxy for assessing how good a particular operating frequency is for a particular qubit is that qubit's relaxation time (T1) at that frequency.” Also ¶ 0056, “… the quantum computing device includes a two dimensional grid of interacting superconducting qubits.”
one or more processors configured to execute computer-readable instructions stored in one or more memory devices to perform operations, the operations comprising: Klimov, ¶ 0005, “One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.”
obtaining characterization data associated with the energy relaxation time for each of the plurality of superconducting qubits at a plurality of possible operating frequencies; Klimov, ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, …” Also ¶ 0066, e.g. “T1,qXY−1(t) represents relaxation time during the frequency sweep qXY, which depends on fqXY(t).”
implementing an … operation to extract one or more predicted … [anomalies] with two-level-system defects from the characterization data for each of the plurality of superconducting qubits; and Klimov ¶ 0048, “For example, the input data 206 may include data representing properties of qubits included in the quantum computing device, such as … predicted and/or measured relaxation and/or coherence times of the qubits included in the quantum computing device.” Also ¶ 0058, “The system defines a first cost function that maps qubit operation frequency values (e.g., all qubit idling frequencies, as described below) to a cost (e.g., a real number) corresponding to an operating state of the quantum device (step 302). e.g., an operating state that executes an arbitrary quantum algorithm with lower error rates compared to other operating states.” ¶ 0102, “It is understood that the term “qubit” encompasses all quantum systems that may be suitably approximated as a two-level system in the corresponding context.”
Klimov does not expressly disclose: unsupervised learning to extract predicted anomalies or collisions with defects.
However, Zou teaches unsupervised learning. See Zou, ¶ 0087, “Thus, in one embodiment, the machine-learning logic/circuit 1008 performs unsupervised learning of new errors as they occur. Unsupervised learning is particularly beneficial for working with a quantum processor 207 because the physical responses of the individual qbits may change over time and may also vary from one quantum processor to another.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Zou’s unsupervised learning with Klimov’s input data in order to identify errors and make corrections as suggested by Zou (¶ 0082 and 0085-0087).
Also, Hertzberg teaches qubit collision defects. See Hertzberg ¶ 0042, “a qubit may have quantum interactions with other proximate qubits, based on their resonance frequency. Such behavior constitutes a failure mode known as a “frequency collision.” Frequency collisions can be predicted by modeling of the quantum-mechanical system.” Also ¶ 0083, “The set-point for each qubit may be determined based on frequency collision model 718, by modeling a plurality of frequencies for the qubits to determine collision probabilities of the system, and optimizing the relative values of the frequencies of the various qubits in the system, to minimize measured frequency collisions and consequently improve the multi-qubit gate fidelities.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Hertzberg’s collision prediction with the unsupervised learning of two-level system defects of Klimov and Zou in order to minimize frequency collision defects and consequently improve the multi-qubit gate fidelities and achieve high performance as suggested by Hertzberg (¶ 0042, 0061 and 0083).
Klimov also discloses:
modifying an operating frequency for each of the plurality of superconducting qubits based at least in part on the one or more predicted collisions with two-level-system defects. Klimov, ¶ 0052, “The optimizer module 210 is configured to adjust qubit operation frequency values to vary a cost according to the adjusted cost function defined by the cost function adjuster 204 such that an operating state of the quantum device specified by the input data 206 is improved, e.g., computations performed by the quantum computing device using the adjusted qubit operation frequency values are less error-prone.”
In regard to claim 18, Klimov and Zou also teach:
18. The quantum computing system of claim 16, wherein implementing an unsupervised learning operation to extract one or more predicted collisions with two-level-system defects from the characterization data for each of the plurality of superconducting qubits comprises implementing the unsupervised learning operation to extract the one or more predicted collisions with two-level-system defects from the characterization data for each of the plurality of superconducting qubits in parallel. See Klimov, ¶ 0046, “In addition, the methods may break the optimization problem into multiple independent sub-problems that may be solved quickly and in parallel using standard optimization techniques.”
In regard to claim 19, Klimov and Hertzberg also teach:
19. The quantum computing system of claim 16, wherein the quantum computing system is configured to implement a quantum gate on one or more of the plurality of superconducting qubits based at least in part on the one or more predicted collisions with two-level-system defects. Klimov, ¶ 0064, “To perform a two-qubit computational gate, the participating qubits are brought into resonance.” Also, see Hertzberg ¶ 0083, “The set-point for each qubit may be determined based on frequency collision model 718 … and consequently improve the multi-qubit gate fidelities.”
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klimov in view of Zou and Hertzberg as applied above, and further in view of Phillipps.
In regard to claim 17, parent claim 16 is addressed above.
All further limitations of claim 17 have been addressed in the above rejection of claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 20240412093 by Papic et al. See Abstract, “… obtaining information on one or more error sources affecting dynamics of at least one qubit, … providing the at least one characterizing signal as an input to a neural network trained to predict information on the one or more error sources affecting the at least one characterizing signal …” Also ¶ 0089, “Hence, proper data pre-processing is beneficial for the optimal training and results of a neural network.” Also ¶ 0091, “For example, the principal component analysis (PCA) algorithm 350 may be used for data dimensionality reduction.”
U.S. Patent Application Publication 20190044542 by Hogaboam et al. See Abstract, “Apparatus and method for neural network learning to detect and correct quantum errors. … a neural network to evaluate the error syndrome and to either identify a known corrective response for correcting the error or to perform unsupervised learning to identify a corrective response to the error syndrome.” Hogaboam, ¶ 0051, “In one embodiment, a neural network 614 performs unsupervised learning of error syndromes through execution of a diagnostic learning cycle during idle cycles of the quantum computer.”
WO2021118867A1 by Klimov. See Abstract, “The method includes determining, by the one or more computing devices, a value for the at least one qubit parameter based at least in part on the calibration data using a de-corrupting autoencoder.” Note that autoencoders rely upon unsupervised learning.
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/James D. Rutten/Primary Examiner, Art Unit 2121