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. 1. This action is in response to the communication filed on August 2, 2023. Claims 1-20 were originally received for consideration. No preliminary amendment has been received for the claims. 2. Claims 1-20 are currently pending consideration. Information Disclosure Statement 3. Initialed and dated copies of Applicant’s IDS (form 1449), received on 7/18/2024, 1/12/2026, and 2/17/2026, are attached to this Office Action. 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. 4. Claim (s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Erhard et al. (“Characterizing large-scale quantum computers via a cycle benchmarking”) in view of Hashim et al. (“Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor”) in further in view of Huang et al. (U.S. Patent Pub. No. US 2022/0309373). Regarding claim 1, E r hard discloses: A method for evaluating scalability of quantum systems (paragraph 1: a major challenge towards practically useful quantum computing is characterizing and reducing the various errors that accumulate in quantum circuits ) , the method comprising: obtaining benchmark performance data for a candidate quantum system architecture (paragraph 1, paragraph 6: develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors ) , the benchmark performance data descriptive of one or more performance characteristics of the candidate quantum system architecture (paragraph 1: the benchmarking protocol yields figures of merit, in particular process fidelity/error rates, which are performance characteristics of the quantum processors, and a particular characterization typically yields some figure of merit, such as the process fidelity) for a plurality of processor sizes of the candidate quantum system architecture (paragraph 1: demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, with total process fidelities for multi-qubit entangling gates ranging from 99.6% for 2 qubits to 86% for 10 qubits ); obtaining one or more scaling parameters based on the benchmark performance data (paragraph 1: benchmark results as a function of system size and derives conclusions about the dependence or independence of error rates on processor size, which constitutes a scaling parameter, as in paragraph 1: “cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupled does not increase with increasing system size, therefore E r hard analyzes benchmark data as a function of processor size). E r hard does not explicitly disclose that the one or more quantum scaling parameters comprising a quantum scaling model relating processor size of the candidate quantum system architecture to the one or more performance characteristics of the candidate quantum system architecture , determining one or more scaling metrics for the candidate quantum system architecture at a scaled processor size greater than the plurality of processor sizes by the quantum scaling model and determining one or more control actions for an operational quantum system based on the one or more scaling metrics. In an analogous art, Hashim discloses experimentally measuring benchmarking error rates can be used as inputs to a predictive model, and that the model can be used to predict performance of quantum computations beyond the directly measured regime (paragraph 1: randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically lowering unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error ratees measured via cycle benchmarking.” Hashim further discloses the use of a model relating measured error rates to computational performance and discloses that prediction of performance in regimes that are not directly measured (see Paragraph 1, and section on randomized compiling protocol). It would have been obvious to use this predictive modeling of Hashim in the system of E r hard in order to reduce unpredictable errors in quantum algorithms and enable accurate predictions of algorithmic performance (paragraph 1). The combination of E r hard and Hashim does not explicitly disclose determining one or more control actions for an operational quantum system based on the one or more scaling metrics. In an analogous art, Huang discloses obtaining a performance metric (fidelity), a control action (updating a gate parameter) is based on that value of the quantum gate and updating a parameter of the quantum gate based on the fidelity value (paragraph 0107). It would have been obvious to provide this control action based on a performance metric in order to provide an updated quantum system based on values (paragraphs 0106-0107). Claim 2 is rejected as applied above in rejecting claim 1. Furthermore, Hashim discloses: The method of claim 1, wherein obtaining the benchmark performance data comprises: selecting a quantum algorithm (page 4, paragraph 3: input states to quantum algorithms ) ; selecting one or more performance benchmarks to be representative of the quantum algorithm (page 2, paragraph 5 : to evaluate the efficacy of RC, we assess the algorithmic performance by the total variation distance ) ; and obtaining the benchmark performance data based on the one or more performance benchmarks ( page 7: quantum fourier transform: each bare QFT was measured 10,000 times ) . Claim 3 is rejected as applied above in rejecting claim 1. Furthermore, Erhard discloses: The method of claim 1, wherein obtaining the benchmark performance data comprises: selecting a hardware architecture of the candidate quantum system architecture, the hardware architecture of the candidate quantum system architecture defining structural characteristics of one or more qubits of the candidate quantum system architecture (page 6, paragraph 1: cycle benchmarking can be readily implemented on general quantum computing architectures to estimate the fidelity of multi-qubit processes ) ; selecting a control strategy of the candidate quantum system architecture, the control strategy defining operational characteristics of the one or more qubits of the candidate quantum system architecture (page 6, paragraph 1: cycle benchmarking can be readily implemented on general quantum computing architectures to estimate the fidelity of multi-qubit processes ) ; and obtaining the benchmark performance data for the candidate quantum system architecture comprising the one or more qubits configured according to the hardware architecture and the control strategy (page 6, paragraph 1: cycle benchmarking can be readily implemented on general quantum computing architectures to estimate the fidelity of multi-qubit processes ) . Claim 5 is rejected as applied above in rejecting claim 3. Furthermore, Erhard discloses: The method of claim 3, wherein the structural characteristics of the one or more qubits comprise qubit type characteristics, qubit arrangement characteristics, control signal line arrangement characteristics, fabrication characteristics, and/or dependency characteristics (page 1, paragraphs 1-3: these characteristics representing inherent and well-known physical and operational properties of quantum processors and their control, which are routinely considered by one of ordinary skill in the art when designing operating, and benchmarking quantum computing systems ) . Claim 6 is rejected as applied above in rejecting claim 3. Furthermore, Erhard discloses: The method of claim 3, wherein the structural characteristics of the one or more qubits comprise at least one of self-capacitance, junction resistance, qubit anharmonicity, qubit- control mutual inductance distribution, maximum frequency, readout-resonator frequency, Josephson-junction asymmetry, two-level-system (TLS) TLS number density, TLS frequency, TLS coherence, TLS qubit-decoupling, qubit quality, qubit-control mutual inductance prime distribution, drive impedance, resonator internal quality, resonator coupling quality, resonator- qubit coupling efficiency, bandpass filter frequency, bandpass filter quality, transmon frequency, T1 spectrum, single qubit frequency, or qubit grid frequency (page 1, paragraphs 1-3: these characteristics representing inherent and well-known physical and operational properties of quantum processors and their control, which are routinely considered by one of ordinary skill in the art when designing operating, and benchmarking quantum computing systems ). Claim 6 is rejected as applied above in rejecting claim 3. Furthermore, Erhard discloses: The method of claim 3, wherein the operational characteristics of the one or more qubits comprise one or more operating frequencies of the one or more qubits (page 1, paragraphs 1-3: these characteristics representing inherent and well-known physical and operational properties of quantum processors and their control, which are routinely considered by one of ordinary skill in the art when designing operating, and benchmarking quantum computing systems ) . Claim 7 is rejected as applied above in rejecting claim 3. Furthermore, Erhard discloses: The method of claim 3, wherein the operational characteristics of the one or more qubits comprise one or more of single qubit gate frequency trajectories, two qubit gate frequency trajectories, readout frequency trajectories, maximum/minimum operating frequencies, anharmonicity of frequencies, bias voltage, coupling efficiency, Ramsey coherence time, spin- echo coherence time, CPMG dephasing time, energy-relaxation time, Rabi oscillations, pulse amplitude, pulse length, pulse frequency, single-qubit randomized benchmarking (RB) error, single-qubit cross-entropy benchmarking (XEB) error, two-qubit RB error, two-qubit XEB error, or two-qubit XEB purity error (page 1, paragraphs 1-3: these characteristics representing inherent and well-known physical and operational properties of quantum processors and their control, which are routinely considered by one of ordinary skill in the art when designing operating, and benchmarking quantum computing systems ). Claim 8 is rejected as applied above in rejecting claim 3. Furthermore, Erhard discloses: The method of claim 3, wherein the operational characteristics of the one or more qubits comprise one or more uncontrolled characteristics (page 1, paragraphs 1-3: these characteristics representing inherent and well-known physical and operational properties of quantum processors and their control, which are routinely considered by one of ordinary skill in the art when designing operating, and benchmarking quantum computing systems ). Claim 9 is rejected as applied above in rejecting claim 3. Furthermore, Erhard discloses: The method of claim 3, wherein the control strategy is indicative of one or more calibration interventions or one or more optimization interventions (page 1, paragraphs 1-3: these characteristics representing inherent and well-known physical and operational properties of quantum processors and their control, which are routinely considered by one of ordinary skill in the art when designing operating, and benchmarking quantum computing systems ). Claim 10 is rejected as applied above in rejecting claim 1. Furthermore, Erhard discloses: The method of claim 1, wherein the benchmark performance data comprises gate error benchmark performance data, and wherein the performance characteristics of the candidate quantum system architecture comprises gate errors (page 5, paragraphs 2-3 : Measuring gate fidelities ) . Claim 11 is rejected as applied above in rejecting claim 1. Furthermore, though Erhard does not explicitly disclose wherein the quantum scaling parameters comprise a qubit saturation constant, a saturated gate error, and a scaling logic error penalty , the use of parametric analytical models, including the use of saturation constants and gate errors, to fit empirical performance data represents a routine modeling approach in quantum computing. Therefore, it would have been obvious to one of ordinary skill in the art to use these parameters in the models. Claim 12 is rejected as applied above in rejecting claim 11. Furthermore, though Erhard does not explicitly disclose wherein the quantum scaling model relates an average gate error at a number of qubits to a quantity comprising the scaling logic error penalty multiplied by the exponential number of qubits divided by the qubit saturation constant, the quantity subtracted from the saturated gate error , the use of parametric analytical models, including the use of saturation constants and gate errors, to fit empirical performance data represents a routine modeling approach in quantum computing. Therefore, it would have been obvious to one of ordinary skill in the art to use these parameters in the models. Claim 13 is rejected as applied above in rejecting claim 1. Furthermore, Erhard discloses: The method of claim 1, wherein obtaining the one or more quantum scaling parameters comprise fitting the quantum scaling model to the benchmark performance data ( paragraph 1, paragraph 6: develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors ) Claim 14 is rejected as applied above in rejecting claim 1. Furthermore, Huang discloses: The method of claim 1, wherein determining the one or more control actions comprises implementing one or more operational characteristics of the candidate quantum system architecture in an operational quantum system (paragraphs 0106-0107). Claim 15 is rejected as applied above in rejecting claim 1. Furthermore, Erhard does not explicitly disclose obtaining second benchmark performance data for a second candidate quantum system A rchitecture , obtaining one or more second scaling parameters based on the second benchmark performance data , determining one or more second scaling metrics for the second candidate quantum system architecture based on the one or more second scaling parameters , comparing the one or more scaling metrics to the one or more second scaling metrics and, based on the comparison, selecting one of the candidate quantum system architecture or the second quantum system and configuring the operational quantum system according to one or more operational characteristics of the selected one of the candidate quantum system architecture or the second candidate quantum system architecture. However, this claim merely represents a well-known adjustment to the system of Erhard to allow an additional input. Therefore, one of ordinary skill in the art would know to perform when comparing multiple candidate quantum architectures. Regarding claim 16, Erhard discloses: A quantum computing system, comprising: quantum hardware (paragraph 1: quantum computer ) ; one or more classical processors (paragraph 1: processors ) ; one or more non-transitory, computer-readable media storing instructions that, when implemented, cause the one or more classical processors to perform operations, the operations comprising: obtaining benchmark performance data for a candidate quantum system architecture (paragraph 1, paragraph 6: develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors ), the benchmark performance data descriptive of one or more performance characteristics of the candidate quantum system architecture (paragraph 1: the benchmarking protocol yields figures of merit, in particular process fidelity/error rates, which are performance characteristics of the quantum processors, and a particular characterization typically yields some figure of merit, such as the process fidelity) for a plurality of processor sizes of the candidate quantum system architecture (paragraph 1: demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, with total process fidelities for multi-qubit entangling gates ranging from 99.6% for 2 qubits to 86% for 10 qubits ); obtaining one or more scaling parameters based on the benchmark performance data (paragraph 1: benchmark results as a function of system size and derives conclusions about the dependence or independence of error rates on processor size, which constitutes a scaling parameter, as in paragraph 1: “cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupled does not increase with increasing system size, therefore Erhard analyzes benchmark data as a function of processor size). Erhard does not explicitly disclose that the one or more quantum scaling parameters comprising a quantum scaling model relating processor size of the candidate quantum system architecture to the one or more performance characteristics of the candidate quantum system architecture, determining one or more scaling metrics for the candidate quantum system architecture at a scaled processor size greater than the plurality of processor sizes by the quantum scaling model and determining one or more control actions for an operational quantum system based on the one or more scaling metrics. In an analogous art, Hashim discloses experimentally measuring benchmarking error rates can be used as inputs to a predictive model, and that the model can be used to predict performance of quantum computations beyond the directly measured regime (paragraph 1: randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically lowering unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error ratees measured via cycle benchmarking.” Hashim further discloses the use of a model relating measured error rates to computational performance and discloses that prediction of performance in regimes that are not directly measured (see Paragraph 1, and section on randomized compiling protocol). It would have been obvious to use this predictive modeling of Hashim in the system of Erhard in order to reduce unpredictable errors in quantum algorithms and enable accurate predictions of algorithmic performance (paragraph 1). The combination of Erhard and Hashim does not explicitly disclose determining one or more control actions for an operational quantum system based on the one or more scaling metrics. In an analogous art, Huang discloses obtaining a performance metric (fidelity), a control action (updating a gate parameter) is based on that value of the quantum gate and updating a parameter of the quantum gate based on the fidelity value (paragraph 0107). It would have been obvious to provide this control action based on a performance metric in order to provide an updated quantum system based on values (paragraphs 0106-0107). Claim 17 is rejected as applied above in rejecting claim 16. Furthermore, Erhard discloses: The quantum computing system of claim 16, wherein obtaining the benchmark performance data comprises: selecting a hardware architecture of the candidate quantum system architecture, the hardware architecture of the candidate quantum system architecture defining structural characteristics of one or more qubits of the candidate quantum system architecture (page 6, paragraph 1: cycle benchmarking can be readily implemented on general quantum computing architectures to estimate the fidelity of multi-qubit processes ); selecting a control strategy of the candidate quantum system architecture, the control strategy defining operational characteristics of the one or more qubits of the candidate quantum system architecture (page 6, paragraph 1: cycle benchmarking can be readily implemented on general quantum computing architectures to estimate the fidelity of multi-qubit processes ); and obtaining the benchmark performance data for the candidate quantum system architecture comprising the one or more qubits configured according to the hardware architecture and the control strategy (page 6, paragraph 1: cycle benchmarking can be readily implemented on general quantum computing architectures to estimate the fidelity of multi-qubit processes ). Claim 18 is rejected as applied above in rejecting claim 16. Furthermore, Erhard discloses: The quantum computing system of claim 16, wherein obtaining the one or more quantum scaling parameters comprises fitting the scaling model to the benchmark performance data (paragraph 1, paragraph 6: develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors ) . Claim 19 is rejected as applied above in rejecting claim 16. Furthermore, though Erhard does not explicitly disclose wherein the quantum scaling parameters comprise a qubit saturation constant, a saturated gate error, and a scaling logic error penalty, the use of parametric analytical models, including the use of saturation constants and gate errors, to fit empirical performance data represents a routine modeling approach in quantum computing. Therefore, it would have been obvious to one of ordinary skill in the art to use these parameters in the models. Regarding claim 20, Erhard discloses: One or more non-transitory, computer-readable media storing instructions that, when implemented, cause one or more processors to perform operations, the operations comprising: obtaining benchmark performance data for a candidate quantum system architecture (paragraph 1, paragraph 6: develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors ), the benchmark performance data descriptive of one or more performance characteristics of the candidate quantum system architecture (paragraph 1: the benchmarking protocol yields figures of merit, in particular process fidelity/error rates, which are performance characteristics of the quantum processors, and a particular characterization typically yields some figure of merit, such as the process fidelity) for a plurality of processor sizes of the candidate quantum system architecture (paragraph 1: demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, with total process fidelities for multi-qubit entangling gates ranging from 99.6% for 2 qubits to 86% for 10 qubits ); obtaining one or more scaling parameters based on the benchmark performance data (paragraph 1: benchmark results as a function of system size and derives conclusions about the dependence or independence of error rates on processor size, which constitutes a scaling parameter, as in paragraph 1: “cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupled does not increase with increasing system size, therefore Erhard analyzes benchmark data as a function of processor size). Erhard does not explicitly disclose that the one or more quantum scaling parameters comprising a quantum scaling model relating processor size of the candidate quantum system architecture to the one or more performance characteristics of the candidate quantum system architecture, determining one or more scaling metrics for the candidate quantum system architecture at a scaled processor size greater than the plurality of processor sizes by the quantum scaling model and determining one or more control actions for an operational quantum system based on the one or more scaling metrics. In an analogous art, Hashim discloses experimentally measuring benchmarking error rates can be used as inputs to a predictive model, and that the model can be used to predict performance of quantum computations beyond the directly measured regime (paragraph 1: randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically lowering unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error ratees measured via cycle benchmarking.” Hashim further discloses the use of a model relating measured error rates to computational performance and discloses that prediction of performance in regimes that are not directly measured (see Paragraph 1, and section on randomized compiling protocol). It would have been obvious to use this predictive modeling of Hashim in the system of Erhard in order to reduce unpredictable errors in quantum algorithms and enable accurate predictions of algorithmic performance (paragraph 1). The combination of Erhard and Hashim does not explicitly disclose determining one or more control actions for an operational quantum system based on the one or more scaling metrics. In an analogous art, Huang discloses obtaining a performance metric (fidelity), a control action (updating a gate parameter) is based on that value of the quantum gate and updating a parameter of the quantum gate based on the fidelity value (paragraph 0107). It would have been obvious to provide this control action based on a performance metric in order to provide an updated quantum system based on values (paragraphs 0106-0107). 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