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 .
Examiner Comments
The preliminary amendment filed April 5, 2024 is acknowledged.
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 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed "at least one memory including computer program code" can be interpreted to include both transitory and non-transitory embodiments. Transitory embodiments are not directed to statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007).
A claim drawn to such a “memory” that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation "non-transitory" to the claim.
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.
Claims 1-20 are 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.
Referring to claims 1, 18, and 20, each claim recites, “a method for obtaining information on one or more error sources…” in the preamble. However, later in each claim, recited is “receiving as an output , from the neural network, information on the one or more error sources…” (see claim 1, lines 9-10, for example). Presumably, the “information” recited at the claim is referring to the same “information” from the preamble though it is not clear as written. Furthermore, lines 7-8 recites, “…to predict information…” Multiple instances of the term “information” render the claims unclear.
Referring to claims 1, 18, and 20, each claim recites, “receiving at least one characterizing measurement of the at least one qubit configured to act as a sensor for the one or more error sources…” (see claim 1, lines 3-4, for example). As written, it’s not clear what is being claimed to be “configured to act as a sensor”. Is it referring to the “at least one characterizing measurement” or “the at least one qubit”?
Claim 14, line 3 recites, “ii) performing following in a sequence:” This seems grammatically incorrect and not clear.
Claim 19 recites the limitation "the performance" in the last line. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 6, 8, 9, and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Machine learning approach for quantum non-Markovian noise classification to Martina et al (herein referred to as Martina, found in Applicant’s filed IDS).
Referring to claims 1, and 18-20, Martina discloses a method for obtaining information on one or more error sources affecting dynamics of at least one qubit, the method comprising: receiving at least one characterizing measurement of the at least one qubit configured to act as a sensor for the one or more error sources affecting the dynamics of the at least one qubit (page 2, column 1, lines 9-17); determining, based on the at least one characterizing measurement, at least one characterizing signal, which describes the dynamics of the at least one qubit (page 2, column 2, line 1 – page 3, column 1, line 30); 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 (page 10, column 1, lines 5-6); and receiving as an output, from the neural network, information on the one or more error sources affecting the at least one characterizing signal (page 10, column 1, lines 5-6).
Referring to claim 2, Martina discloses wherein the at least one characterizing measurement is at least one of: a single gate experiment comprising repeating applications of the single gate with varying time delays and performing a qubit population measurement; a two-qubit gate experiment comprising repeating applications of the two-qubit gate with equal or varying time delays and performing a qubit population measurement (population measurement, see page 2, column 1, lines 9-12);
Referring to claim 3, Martina discloses wherein the one or more error sources comprise one or more of: Markovian decoherence (page 2, column 1, lines 9-12).
Referring to claim 6, Martina discloses wherein the neural network is trained using training data comprising synthetic signals describing dynamics of the at least one qubit, wherein the synthetic signals are generated by simulation using a theoretical model describing the dynamics of the at least one qubit and the one or more error sources (synthetic training data, page 11, column 2, lines 20-30).
Referring to claim 8, Martina discloses wherein the synthetic signals are divided into a training subset and a testing subset; and wherein the method comprises: providing the training subset as input to the neural network; optimizing parameters of the neural network by updating the parameters to minimize a difference between output values of the neural network and actual values of the training subset; and validating performance of the neural network using the testing subset (synthetic training data, page 11, column 2, lines 20-30).
Referring to claim 9, Martina discloses wherein the synthetic signals are generated using different values for a set of parameters modelling the one or more error sources (synthetic training data, page 11, column 2, lines 20-30).
Referring to claim 13, Martina discloses wherein the neural network is trained by applying supervised learning on known training data via stochastic gradient descent and any classical optimizer, wherein the training is performed by minimizing a loss function (page 6, column 1, lines 52-54).
Referring to claim 14, Martina discloses wherein the synthetic signals are generated by: i)—simulating a qubit gate with different error sources sequentially; or ii) performing following in a sequence: simulating a qubit gate with a first error source; simulating the qubit gate with a first error source and a second error source, which is different than the first error source; and simulating the qubit gate with a first error source, a second error source and a third error source, which are all different (synthetic training data, page 11, column 2, lines 20-30).
Referring to claim 15, Martina discloses comprising: determining a relative contribution of different error sources to the dynamics of the at least one qubit by evaluating infidelity of the at least one qubit in response to simulation with the different error sources (synthetic training data, page 11, column 2, lines 20-30).
Referring to claim 16, Martina discloses wherein the information on the one or more error sources received as the output from the neural network is indicative of the relative contribution of different error sources to the at least one characterizing signal (synthetic training data, page 11, column 2, lines 20-30).
Referring to claim 17, Martina discloses wherein the one or more error sources comprise a first error source and a second error source; and the relative contribution of different error sources is given as relative contribution values such that a first relative contribution value indicates to which extent the at least one characterizing signal is affected by the first error source and a second relative contribution value indicates to which extent the at least one characterizing signal is affected by the second error source (synthetic training data, page 11, column 2, lines 20-30).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Justin Knapp whose telephone number is (571)270-3008. The examiner can normally be reached 8:00 am - 4:30 pm (ET).
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Justin R. Knapp
Primary Examiner
Art Unit 2112
/JUSTIN R KNAPP/Primary Examiner, Art Unit 2112
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