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 .
This action is responsive to the claims filed 5/17/2023.
Claims 1-20 are presented for examination.
Claim Objections
Claims 1, 3, 12, 14 and 16 are objected to because of the following informalities:
Claim limitations in claim 1 (lines 5-6), claim 12 (lines 6-7), and claim 14 (lines 8-9) each recite in part “using the submitted quantum code-related entities”, these should be --using the obtained quantum code-related entities-- as otherwise there is a lack of antecedent basis for these claims.
Claim limitations in claim 3 (line 3) and claim 16 (line 3) each recite in part “the simulation”, these should be --a simulation-- as otherwise there is a lack of antecedent basis for these claims.
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.
Claims 3-5, 9-11, 16, and 19-20 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.
Claims 3 (line 1), 9 (line 2), 10 (line3), 16 (line 1), 19 (line 2), and 20 (line 3) each recites the limitation "the quantum code-related entity". There is insufficient antecedent basis for this limitation in the said claims. Independent claims 1 and 14, from which these said claims variously depend from, recite obtaining "quantum code-related entities" (plural). However, the dependent claims refer back to this limitation in the singular: "wherein the quantum code-related entity comprises..." (e.g., Claim 3). This creates ambiguity as to whether the dependent claim is referring to one specific entity out of a plurality, or if it is improperly referencing the plural group as a singular item. Thus, the said claims are indefinite. For the purposes of examination the said claim limitations will be interpreted as “at least one of the quantum code-related entities”
Claims 4-5 and 11 do not cure the 35 USC 112 (b) issue cause by their respective parent claims and thus are also rejected under 35 USC 112 (b) for at least being dependent on a rejected parent claim.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1Step 1: The claim recites “A method comprising:”; therefore, it is directed to the statutory category of a process.Step 2A Prong 1: The claim recites, inter alia:selecting one or more of a set of one or more trained neural network models for inferencing based on the quantum code-related entities: These limitations recite a mentally performable process of using judgement to select from one or more of an observed set of one or more trained neural network models intended for inferencing based on the observed quantum code-related entities.
Thus the claim recites a judicial exception.Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:obtaining quantum code-related entities; and returning a result of the inferencing operation: These additional elements merely recite insignificant extra-solution activity of mere data gathering, e.g., obtaining entities, and data outputting, e.g., returning a result, as all uses of the judicial exception of inferencing require the obtained entities and outputting the result. See MPEP 2106.05(g).
using a hardware computing device: These additional elements are recited at a high level of generality and merely amount to invoking computers or other machinery merely as a tool to apply the underlying judicial exception corresponding to selecting models. See MPEP 2106.05(f).performing, using the hardware computing device, the inferencing using the submitted quantum code-related entities and the selected one or more trained neural network models: These additional elements are recited at a high level of generality reciting results of performing inferencing with no inventive particulars or details as to how selection one or more trained neural networks is accomplished and no details how the submitted quantum code-related entities are used, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of determining training samples.Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activity of data gathering and data outputting recited by "obtaining quantum code-related entities" and "returning a result of the inferencing operation" which are well-understood, routine, and conventional activities similar to receiving or transmitting data over a network and presenting offers and gathering statistics as described in MPEP 2106.05(d)(II). Additionally the additional elements include invoking computer machinery to apply the underlying judicial exception and “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2Step 1: a process as in claim 1.Step 2A Prong 1: The claim recites the same abstract idea as claim 1 as the judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein one or more running options comprise a configuration of a target quantum calculator to be simulated and wherein the selecting operation is based on the one or more running options: These limitations are recited at a high level of generality and represents the field of use or technological environment in which to practice the underlying abstract idea, e.g. the selecting operation. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the underlying judicial exception to a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3Step 1: a process as in claim 1.Step 2A Prong 1: The claim recites, inter alia:and the method further comprises debugging the quantum program based on the results of the simulation of the quantum program: These limitations recite further mental processes of using human judgment to debug a quantum program based on observation of the results of the simulation.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows: wherein the quantum code-related entity (interpreted as at least one of the quantum code-related entities per the 35 USC 112(b) rejection set forth above) comprises a quantum program: These additional elements are recited at a high level of generality and represents the field of use or technological environment in which to practice the underlying abstract idea, e.g. debugging a quantum program based on the results of the simulation. See MPEP 2106.05(h).
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the underlying judicial exception to a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 4Step 1: a process as in claim 3.Step 2A Prong 1: The claim recites the abstract ideas of claim 3 as the judicial exception.Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:further comprising deploying the debugged program: These additional elements are recited at a high level of generality reciting results of deploying the debugged program with no inventive particulars or details as to the deployment, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of debugging the quantum program. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 5Step 1: a process as in claim 4.Step 2A Prong 1: The claim recites the abstract ideas of claim 4 as the judicial exception..Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:further comprising executing the deployed debugged program: These additional elements are recited at a high level of generality reciting results of executing the deployed debugged program with no inventive particulars or details as to the executing, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of debugging the quantum program. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6Step 1: a process as in claim 1.Step 2A Prong 1: The claim recites the abstract idea of claim 1 as the judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:wherein the inferencing operation is performed using a plurality of the trained neural network models: These additional elements are recited at a high level of generality and represents the field of use or technological environment in which to practice the underlying abstract idea, e.g. selecting trained neural network models for inferencing. See MPEP 2106.05(h).
and generates quantum simulation results for a plurality of different given target quantum calculators: These additional elements are recited at a high level of generality reciting results of generating quantum simulation with no inventive particulars or details as to the generation nor target quantum calculators, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of selecting trained neural network models for inferencing. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include generally linking the underlying judicial exception to a field of use or technological environment and “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 7Step 1: a process as in claim 1.Step 2A Prong 1: The claim recites the abstract idea of claim 1 as the judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:wherein the inferencing operation accounts for defined constraints of a target quantum calculator: These additional elements are recited at a high level of generality reciting results of generating quantum simulation with no inventive particulars or details as to the generation nor target quantum calculators, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of selecting trained neural network models for inferencing. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 8Step 1: a process as in claim 1.
Step 2A Prong 1: The claim recites the abstract idea of claim 1 as the judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:wherein the selecting operation is based on a programming language of a quantum program of the quantum code-related entities and a configuration of a target quantum calculator to be simulated by the inferencing: These additional elements are recited at a high level of generality and represents the field of use or technological environment in which to practice the underlying abstract idea, e.g. selecting trained neural network models for inferencing. See MPEP 2106.05(h).Step 2B: The additional elements from Step 2A Prong 2 include generally linking the underlying judicial exception to a field of use or technological environment. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 9Step 1: a process as in claim 1.Step 2A Prong 1: The claim recites, inter alia:further comprising verifying the returned result and comparing the result of the inferencing operation and a result generated by the target quantum machine: These limitations recite further mentally performable processes of using judgement to verify the observed returned result and comparing the results of the observed inferencing operation with that generated by the target quantum machine.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
by running a quantum program of the quantum code-related entity (interpreted as at least one of the quantum code-related entities per the 35 USC 112(b) rejection set forth above) on a target quantum machine: These additional elements are recited at a high level of generality reciting results of running a quantum program on a target quantum machine with no inventive particulars or details how the quantum program is run nor the architecture of a target quantum machine, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of selecting trained neural network models for inferencing. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 10Step 1: a process as in claim 1.Step 2A Prong 1: The claim recites the abstract idea of claim 1 as the judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:further comprising training a developing neural network model to obtain a given one of the set of one or more trained neural network models using a training quantum program of the quantum code-related entity (interpreted as at least one of the quantum code-related entities per the 35 USC 112(b) rejection set forth above), a given result corresponding to the training quantum program, and a configuration of a target quantum calculator: These additional elements are recited at a high level of generality reciting results of training a developing neural network model with no inventive particulars or details how the training is conducted using a training quantum program of the quantum code-related entity, a given result corresponding to the training quantum program, and a configuration of a target quantum calculator, e.g. no details of the specifics of what algorithm the training quantum program uses to conduct supervised/unsupervised/etc. training nor how the given result corresponding to the training quantum program is used, nor what constitutes a configuration of a target quantum calculator, nor architecture/details of the target quantum calculator, thus these limitations merely amounts to “apply it” or equivalent instructions to the abstract idea of selecting trained neural network models for inferencing. See MPEP 2106.05(f).
Step 2B: The additional elements from Step 2A Prong 2 include “apply it” or equivalent instructions. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 11Step 1: a process as in claim 10.Step 2A Prong 1: The claim recites, inter alia:further comprising randomly generating the training quantum program: These limitations recite a mentally performable process with aid of pen and paper using judgement to randomly generate the training quantum program based on the observed quantum code-related entity.Step 2A Prong 2 & Step 2B: There are no additional elements recited so the claim does not provide a practical application and is not considered to be significantly more. As such, the claim is patent ineligible.
Claims 12-13
Step 1: These claims are directed to “A non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform the method of:”; therefore, these claims are directed to the statutory category of an article of manufacture.
Step 2A Prong 1: These claims recite the same abstract ideas as in claims 1-2, respectively.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The only substantive difference between claims 12-13 and claims 1-2 is that claims 12-13 include additional elements of “A non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform the method of”. However, mere recitation that a judicial exception is to be performed using generic computer machinery in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step mirrors that of claims 1-2, respectively.
Step 2B: These claims do not contain significantly more than the judicial exception. The only substantive difference between claims 12-13 and claims 1-2 is that claims 12-13 include additional elements of “A non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform the method of”. However, mere recitation that a judicial exception is to be performed using generic computer machinery in their ordinary capacity cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step mirrors that of claims 1-2, respectively.
Claims 14-20
Step 1: These claims are directed to “An apparatus comprising: a memory; and at least one processor, couple to said memory, and operative to perform operations comprising:”; therefore, these claims are directed to the statutory category of machines.
Step 2A Prong 1: Claims 14-15 and 17-20 recite the same abstract ideas as in claims 1-2, 6, and 8-10, respectively, and claim 16 recites the same abstract ideas as in the combination of claims 3-5.
Step 2A Prong 2: The judicial exception recited in these claims are not integrated into a practical application. The only substantive difference between claims 14-15, 16, and 17-20 and claims 1-2, the combination of claims 3-5, 6, and 8-10 is that claims 14-20 include additional elements of “An apparatus comprising: a memory; and at least one processor, couple to said memory, and operative to perform operations comprising”. However, mere recitation that a judicial exception is to be performed using generic computer machinery in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step mirrors that of claims 1-2, combination of 3-5, 6, and 8-10, respectively.
Step 2B: These claims do not contain significantly more than the judicial exception. The only substantive difference between claims 14-15, 16, and 17-20 and claims 1-2, the combination of claims 3-5, 6, and 8-10 is that claims 14-20 include additional elements of “An apparatus comprising: a memory; and at least one processor, couple to said memory, and operative to perform operations comprising”. However, mere recitation that a judicial exception is to be performed using generic computer machinery in their ordinary capacity cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step mirrors that of claims 1-2, combination of 3-5, 6, and 8-10, respectively.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla et al. (hereinafter Carrasquilla) “Probabilistic simulation of quantum circuits using a deep-learning architecture” (2021) in view of Wang et al. (hereinafter Wang) “QuEst: Graph Transformer for Quantum Circuit Reliability Estimation" (2022).
Regarding independent claim 1, Carrasquilla teaches a method comprising: obtaining quantum code-related entities (page 1 Abstract "simulate quantum circuits using an attention network based on the Transformer", page 3 section II last paragraph "A quantum circuit is a generalization of the circuit model of classical computation where a product state is evolved through a series of unitary gates"; wherein the quantum circuits are quantum code-related entities that are obtained); performing, using hardware computing device, the inferencing using the submitted quantum code-related entities and one or more trained neural network models (page 4 section III paragraph 1 "The strategy to approximate the output distribution Pr consists in constructing models Pθi... based on a rich family of probability distributions... expressed in terms of a neural network with parameters θ", page 5 section IV paragraph 1 "we consider prototypical autoregressive models commonly used in neural machine translation and language modelling based on Transformer encoder blocks"; wherein one or more trained neural network models are executed on a hardware computing device to perform inferencing on the quantum circuits); and returning a result of the inferencing operation (page 6 section V.A paragraph 1 "We use a variety of quality metrics to quantify the efficacy of our method: The KL divergence... the classical fidelity... and the L1 norm of the probability distributions are all designed to measure the difference between the probability distribution of the neural probabilistic model and the exact probability distribution"; wherein the probability distribution is the returned result of the inferencing operation).
Carrasquilla does not explicitly teach selecting, using a hardware computing device, one or more of a set of one or more trained neural network models for inferencing based on the quantum code-related entities.
However, Wang teaches selecting, using a hardware computing device, one or more of a set of one or more trained neural network models for inferencing based on the quantum code-related entities (page 7 section 5.2 paragraph 1 "We train one separate model for each of the backend settings. For results on noisy simulators, the points are close to the y = x line with an R2 value of 0.991. On real machines, the difficulty is greater than on noisy simulators... we select 30 representative quantum algorithms as benchmarks and show the scatter plots for predicted PST on the test set. Each color represents one algorithm circuit under different noise models. We train one common model for the 30 algorithm circuits"; demonstrates selecting a specific trained model from a set of models based on the quantum circuit and backend to perform inferencing).
Because Carrasquilla and Wang address the issue of using neural networks, specifically transformers, to simulate and estimate the performance of quantum circuits, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of selecting a specific trained model from a set of models based on the circuit and backend as suggested by Wang into Carrasquillo’s method, with a reasonable expectation of success, such that a system would be capable of selecting, using a hardware computing device, one or more of a set of one or more trained neural network models for inferencing based on the quantum code-related entities; performing, using the hardware computing device, the inferencing using the submitted quantum code-related entities and the selected one or more trained neural network models . This modification would have been motivated by the desire to provide accurate fidelity prediction for different backend noise configurations and circuit types (Wang page 2 last paragraph).
Regarding dependent claim 2, Carrasquilla, in view of Wang, teach the method of claim 1, wherein one or more running options comprise a configuration of a target quantum calculator to be simulated and wherein the selecting operation is based on the one or more running options (As established in the rejection of claim 1, Wang teaches selecting a specific trained model based on the backend settings. Wang page 6 section 4.2 further teaches that the models take into account "the calibration information of the backend with the following format: [T1, T2 for the first target qubit, T1, T2 for the second target qubit, gate error rate, readout error10, readout error01]"; this suggests the calibration information and backend noise settings constitute a configuration of a target quantum calculator to be simulated (i.e., running options). It would have been obvious to a person of ordinary skill in the art before the effective filing date to base the selection operation on the configuration of the target quantum calculator as taught by Wang to ensure the simulation accurately reflects the specific hardware constraints and noise profile of the intended quantum device per the same motivation recited for the combination of Carrasquilla in view of Wang set forth in claim 1).
Regarding dependent claim 3, Carrasquilla, in view of Wang, teach the method of claim 1, wherein the quantum code-related entity (interpreted as at least one of the quantum code-related entities per the 35 USC 112(b) rejection set forth above) comprises a quantum program (see Wang Abstract paragraph 2 "estimating the noise impact on circuit reliability is an essential step toward understanding and mitigating noise"; suggest the quantum circuits’ noise can be estimated programmatically) and the method further comprises debugging the quantum program based on the results of the simulation of the quantum program (see Wang page 2 left column last paragraph "[i]f the fidelity of a circuit is lower than a threshold, running it on real quantum machines will not generate any meaningful result. One straightforward method is to perform circuit simulation on noisy simulators”; suggest debugging programmatically based on the results of the simulation of the quantum program).
Regarding dependent claim 4, Carrasquilla, in view of Wang, teach the method of claim 3, further comprising deploying the debugged program (see Wang page 2 left column last paragraph teaching that the purpose of the simulation is to evaluate the circuit "before submitting it for execution". It naturally follows that once the circuit has been evaluated and mitigated for noise (debugged), it is submitted or deployed for execution on the quantum hardware. It would have been obvious to a person of ordinary skill in the art to deploy the debugged quantum program on the target quantum machine, as the fundamental purpose of writing and debugging a quantum program is to ultimately execute it).
Regarding dependent claim 5, Carrasquilla, in view of Wang, teach the method of claim 4, further comprising executing the deployed debugged program (see Wang page 2 left column last paragraph teaching that the purpose of the simulation is to evaluate the circuit "before submitting it for execution". It would have been obvious to a person of ordinary skill in the art to execute the deployed debugged program on the target quantum machine to obtain a computational result).
Regarding dependent claim 6, Carrasquilla, in view of Wang, teach the method of claim 1, wherein the inferencing operation is performed using a plurality of the trained neural network models and generates quantum simulation results for a plurality of different given target quantum calculators (see Wang pages 6-7 section 5.2 and Figure 7 teaches training and utilizing separate models for different backend settings and generating results for a plurality of different target quantum calculators, specifically noting "We train one separate model for each of the backend settings" and generating predicted PST (results) for "IBM Geneva, IBM Hanoi, IBM Montreal, IBM Mumbai, and IBM Toronto"; it would have been obvious to a person of ordinary skill in the art to perform inferencing using a plurality of models to generate results for a plurality of different target quantum calculators to compare how the quantum program would perform across different available quantum hardware platforms).
Regarding dependent claim 7, Carrasquilla, in view of Wang, teach the method of claim 1, wherein the inferencing operation accounts for defined constraints of a target quantum calculator (see Wang page 6 section 4.2 teaches that the neural network processes a feature vector for each node that includes "T1 and T2 of the target qubit, gate error, and gate index" which "describe the calibration information of the backend" (defined constraints of a target quantum calculator); it would have been obvious to a person of ordinary skill in the art to account for these defined constraints during inferencing to ensure the simulation accurately models the physical limitations of the target quantum device).
Regarding dependent claim 8, Carrasquilla, in view of Wang, teach the method of claim 1, wherein the selecting operation is based on a programming language of a quantum program of the quantum code-related entities and a configuration of a target quantum calculator to be simulated by the inferencing (see Wang page 6 section 4.2 teaches that the model processes the circuit by extracting a gate graph where "[t]he features include gate type, target qubit index... [and] the calibration information of the backend" wherein the gate types and their sequence (the circuit structure) are construed as a programming language of the quantum program of the quantum code-related entities. Wang also bases the operation on the configuration of the target quantum calculator (backend calibration); it would have been obvious to a person of ordinary skill in the art to base the selection and inferencing on both the programming language (circuit structure) and the hardware configuration to provide a comprehensive and accurate fidelity prediction).
Regarding dependent claim 9, Carrasquilla, in view of Wang, teach the method of claim 1, further comprising verifying the returned result by running a quantum program of the quantum code-related entity (interpreted as at least one of the quantum code-related entities per the 35 USC 112(b) rejection set forth above) on a target quantum machine and comparing the result of the inferencing operation and a result generated by the target quantum machine (Wang pages 6-7 Figure 7 and section 5.2 teaches evaluating the accuracy of the neural network's predictions by comparing the predicted PST (returned result) against the "Ground Truth PST" obtained by running the circuits on real quantum machines, as shown in the scatter plots comparing "Predicted PST" versus "Ground Truth PST" for machines like IBM Geneva and IBM Hanoi; it would have been obvious to a person of ordinary skill in the art to verify the returned simulation results by running the program on a target machine and comparing the results to validate the accuracy of the trained neural network model).
Regarding dependent claim 10, Carrasquilla, in view of Wang, teach the method of claim 1, further comprising training a developing neural network model to obtain a given one of the set of one or more trained neural network models using a training quantum program of the quantum code-related entity (interpreted as at least one of the quantum code-related entities per the 35 USC 112(b) rejection set forth above), a given result corresponding to the training quantum program, and a configuration of a target quantum calculator (see Wang pages 1-2 section 1 and page 7 section 5.1 "Model and Training Setups" teaches the process of training the graph transformer model by collecting a "large dataset containing various randomly generated circuits and circuits from common quantum algorithms" (training quantum program), obtaining "their fidelity on noisy simulators and real machines" (given result), and using the "backends' noise configurations" (configuration of a target quantum calculator); it would have been obvious to a person of ordinary skill in the art to train the neural network model using these specific inputs to enable the model to accurately learn the relationship between circuit structure, hardware noise, and resulting fidelity).
Regarding dependent claim 11, Carrasquilla, in view of Wang, teach the method of claim 10, further comprising randomly generating the training quantum program (see Wang page 4 Figure 3 and section 3.2 teaches that the dataset generation process includes a step to "Generate Random Circuits with Basis Gates" where "random gates are generated from the basis gate set {RZ, SX, X, CNOT} and assigned to quantum circuits to create an initial version of random circuits"; it would have been obvious to a person of ordinary skill in the art to randomly generate the training quantum programs to create a sufficiently large and diverse dataset, which is necessary to properly train a robust machine learning model capable of generalizing to unseen quantum circuits).
Regarding claims 12-13, these are non-transitory computer readable medium claims that are substantially the same as the method of claims 1-2, respectively. Thus, claims 12-13 are rejected for the same reasons as claims 1-2. In addition, Carrasquilla teaches a non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform the method of (Page 9 second to last paragraph "...what can be simulated exactly on classical computers to validate and test quantum computers and algorithms", pages 12-13 Appendix G: Optimization details “The models are optimized using Adam Optimizer [21] in Pytorch [46]... We use single-precision (32-bit) floating-point representation for real numbers"; the use of software frameworks like PyTorch to execute algorithms on classical computers inherently necessitates a non-transitory computer-readable medium storing computer-executable instructions to perform the claimed operations).
Regarding claims 14-20, these are apparatus claims that are substantially the same as the method of claims 1-10, respectively wherein claim 16 is a combination of subject matter in claims 3-5. Thus, claims 14-20 are rejected for the same reasons as claims 1-10. In addition, Carrasquilla teaches an apparatus comprising: a memory; and at least one processor, coupled to said memory, and operative to perform operations comprising (Page 9 second to last paragraph "...what can be simulated exactly on classical computers to validate and test quantum computers and algorithms", pages 12-13 Appendix G: Optimization details “We use single-precision (32-bit) floating-point representation for real numbers. The batch size of each training is around 10^4... VQE circuit simulations with can be completed within a few hours for small system size L and up to one day for L = 18 with one V100 GPU. GHZ circuits and Graph state circuits simulation up to 60 qubits can be completed between one or two days with 4 V100 GPUs in parallel"; disclosure of utilizing classical computers and "V100 GPUs" (processors) to process batch sizes of data and 32-bit floating-point representations suggests an apparatus comprising at least one processor coupled to a memory to perform the claimed operations).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cantori et al. “Supervised learning of random quantum circuits via scalable neural networks” 24 Jun 2022 (ABSTRACT Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are represented via an appropriately designed one-hot encoding of the constituent gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform the quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements).
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/KC CHEN/Primary Patent Examiner, Art Unit 2143