DETAILED ACTION
Notice of 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/9/2026 has been entered.
Response to Amendment
Applicant’s Amendment and remarks submitted on 2/9/2026 have been considered. Claims 1-7, 9-14, 16-18, and 20 are pending.
Response to Arguments
On page 8 of Applicant’s 2/9/2026 Amendment and remarks, Applicant asserts that at least paras. 0025-0026 of the instant specification provide sufficient written description support for the claim amendments.
The examiner agrees that the portions of the disclosure identified by Applicant provide sufficient written description support for the claim amendments.
On pages 7-8 of Applicant’s 2/9/2026 Amendment and remarks, with respect to the rejections of independent claims 1, 9, and 16 under 35 U.S.C. 103, Applicant asserts that the prior art of record does not teach the newly-added “wherein a second composite result set associated with the different instruction set indicates a reduced impact on the one or more performance metrics of the plurality of quantum simulator nodes as compared to the instruction set of the one or more instruction sets” limitation.
The examiner agrees that the prior art of record does not explicitly teach this new limitation. However, new grounds of rejection in view of the KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH references are provided in this action.
On page 9 of Applicant’s 2/9/2026 Amendment and remarks, Applicant argues that the dependent claims should be allowed for the same reasons explained above with respect to independent claims 1, 9, and 16.
The examiner respectfully disagrees for the same reasons explained above with respect to claims 1, 9, and 16.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-6, 9, 12-4, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230153155 A1, hereinafter referenced as KRNETA, in view of US 10592216 B1, hereinafter referenced as RICHARDSON, and further in view of US 20210263733 A1, hereinafter referenced as TANNIRU, and further in view of US 20110145814 A1, hereinafter referenced as MANGIONE-SMITH.
Regarding Claim 1
KRNETA teaches:
A method, comprising: (KRNETA, para. 0076: “The systems and methods may be implemented manually, in software, in hardware, or in a combination thereof.”)
obtaining, by one or more computing devices, a quantum service definition file comprising a plurality of instructions; (KRNETA, para. 0021 and Fig. 1: “In some embodiments, user 116 may provide algorithm execution management system 106, e.g., via user interface 108, request 118 for executing an algorithm using different types of computing resources, including classical computing resources 110 and quantum computing resources 112. In some embodiments, request 118 may indicate a container for the algorithm, for example, container 120. In some embodiments, the container may be retrieved from container repositories 118 of provider network 102. The container may be a package of software that includes the algorithm code and its dependencies so that the algorithm code may be portable and executable from one classical computing resource to another.”;
KRNETA, Fig. 2 and para. 0033: “FIG. 2 shows example contents of a container and interactions between the container and an algorithm execution management system via an application programming interface (API), according to some embodiments. In FIG. 2, in some embodiments, container 202 may include one or more folders, such as folder 204 “opt/jobs/output” and folders 206 “opt/jobs/code,” “opt/jobs/input/data,” “opt/jobs/input/config,” and/or “opt/jobs/checkpoints.” ... Further, container 202 may be associated with a variable “output_data_config” whose value may be specified. Based on the value of the variable “output_data_config,” application programming interface (API) 208 corresponding to an algorithm execution management system (e.g., algorithm execution management system 106) may use the variable “output data config” to copy the data from folder 204 “opt/jobs/output” of container 202 to a location, e.g., a data store of a provider network, specified by the variable “output data config” or a default location if the value is not specified.”
Examiner’s Note (EN): As shown in Fig. 1, user 116 sends a request 118, including container 120, to the quantum computing service 104 over network 114 (corresponding to recited “obtaining” step), where the container corresponds to the recited “quantum service definition file comprising a plurality of instructions” because it’s a container with algorithms and other data for utilizing a quantum service)
determining, by the one or more computing devices, one or more instruction sets from the plurality of instructions; (KRNETA, para. 0021: “For example, in some embodiments, the container may include the code of the algorithm (which may be included in one or more script files), one or more associated libraries for executing the algorithm, runtime (e.g., software or instructions that are executed while the algorithm is executed), and/or one or more system tools and settings (e.g., environment variables).”;
KRNETA, para. 0034: “in some embodiments, the algorithm code may be originally included in container 202, rather than copied from (the location) of the provider network.”;
Examiner’s Note (EN): As shown in Fig. 2, the container has a “opt/jobs/code” directory that includes script files corresponding to recited “one or more instruction sets from the plurality of instructions”)
for each instruction set, communicating, by the one or more computing devices, the instruction set to a plurality of quantum simulator nodes, (KRNETA, para. 0041: “For example, in some embodiments, user 116 may submit an algorithm to be simulated and quantum compute simulator using classical hardware 418 may determine resources needed to perform the simulation job, reserve the resources, configure the resources, etc. In some embodiments, quantum compute simulator using classical hardware 418 may include one or more “warm” simulators that are pre-configured simulators such that they are ready to perform a simulation job without a delay typically involved in reserving resources and configuring the resources to perform simulation.”; (EN): the quantum computing service 104 sends the algorithm to be simulated to the classical hardware 418, which comprises one or more simulators (corresponding to recited “plurality of quantum simulator nodes”))
each quantum simulator node associated with a parameter set for a configuration profile for a quantum computing device such that each quantum simulator node is associated with a different configuration profile for the quantum computing device; (KRNETA, para. 0024: “ In some embodiments, the container may also include one or more environment variables, and user 116 may specify their values to further customize the compute environment.”
KRNETA, para. 0026: “For example, in some embodiments, user 116 may embed an identifier (or ID) of a type of a quantum computing unit (QPU) into a value of an environment variable of the container, and then include the value of the environment variable in the algorithm code.”;
(KRNETA, para. 0041: “For example, in some embodiments, user 116 may submit an algorithm to be simulated and quantum compute simulator using classical hardware 418 may determine resources needed to perform the simulation job, reserve the resources, configure the resources, etc. In some embodiments, quantum compute simulator using classical hardware 418 may include one or more “warm” simulators that are pre-configured simulators such that they are ready to perform a simulation job without a delay typically involved in reserving resources and configuring the resources to perform simulation.”;
(EN): KRNETA discloses that the user may configure the computing resources, or that such resources may be pre-configured, where the combination of pre-configuration settings or user-specified settings corresponds to the recited “different configuration profile”; the examiner further notes that KRNETA discloses using an “environment variable” to specify the ID of a particular type of quantum computing unit”)
for each instruction set, obtaining, by the one or more computing devices, a result set from each quantum simulator node, (KRNETA, para. 0032: “ In some embodiments, algorithm execution management system 106 may receive one or more results from classical computing resources 110 and/or quantum computing resources 112. In some embodiments, the results may be received during execution of the algorithm.”)
However, KRNETA fails to explicitly teach:
the result set comprising data indicative of one or more performance metrics associated with an execution of the instruction set by the quantum computing device configured in accordance with the parameter set associated with the quantum simulator node;
determining, by the one or more computing devices, a first composite result set based at least in part on the result set from each quantum simulator node;
automatically modifying, by the one or more computing devices, based at least in part on the first composite result set, the quantum service definition file by replacing an instruction set of the one or more instruction sets with a different instruction set,
wherein a second composite result set associated with the different instruction set indicates a reduced impact on the one or more performance metrics of the plurality of quantum simulator nodes as compared to the instruction set of the one or more instruction sets.
However, in a related field of endeavor (cloud-based simulation of quantum computing resources, see col. 5, lines 57-61), RICHARDSON teaches:
the result set comprising data indicative of one or more performance metrics associated with an execution of the instruction set by the quantum computing device configured in accordance with the parameter set associated with the quantum simulator node; (RICHARDSON, col. 10, lines 21-27: “Quantum algorithms may be considered probabilistic such that they provide a solution with a certain probability, and the algorithm 165 may be run more than once to arrive at different results 117. In one embodiment, by repeatedly setting the algorithm's initial values, running the algorithm, and measuring the algorithm's results, the probability of arriving at the correct answer may be increased.”
RICHARDSON, col. 25, lines 27-38: “In one embodiment, the quantum resource(s) 162A and classical resource(s) 172A may both be used for comparison of their respective results. In one embodiment, the quantum resource(s) 162A and classical resource(s) 172A may both be used for comparison of their respective runtime and/or cost to generate results. In one embodiment, the results 169A and 179A may be aggregated into a final result 669 which is then provided to the client 140. In one embodiment, the final result 669 may be selected from among the results 169A and 179A and provided to the client 140, e.g., based on the speed or accuracy of the individual results.”;
Examiner’s Note: RICHARDSON discloses determining performance metrics with respect to accuracy, runtime, and and/or costs with respect to quantum results simulated on a classical computer; the KRNETA-RICHARDSON combination now modifies the quantum simulators of KRNETA to also determine the accuracy, runtime, and/or costs associated with each quantum algorithm as disclosed by RICHARDSON)
determining, by the one or more computing devices, a first composite result set based at least in part on the result set from each quantum simulator node. (RICHARDSON, col. 22, lines 37-45: “In one embodiment, the task management service 600 may include a component for results aggregation 640. In one embodiment, the results aggregation 640 may collect and aggregate results of multiple runs of the same quantum algorithm on one or more quantum computing resources. The multiple runs may be performed in serial or in parallel. In one embodiment, the multiple runs of the same algorithm may increase the accuracy of the aggregate results due to the probabilistic nature of quantum computing.”; (EN): the KRNETA-RICHARDSON combination now modifies the quantum simulators of KRNETA to perform multiple runs of each algorithm to average them for accuracy in view of the ”probabilistic nature of quantum computing.”)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON as explained above. As disclosed by RICHARDSON, one of ordinary skill would have been motivated to do so because averaging results over multiple runs increases the accuracy of a quantum simulation in view of the “probabilistic nature of quantum computing.” (col. 22, lines 37-45). As further disclosed by RICHARDSON, one of ordinary skill would have been motivated to do so in order for “ the quantum computing simulation service 1500 may recommend a number of classical resources to a client, e.g., based on metrics for past simulations.” (col. 35, lines 32-35).
However, KRNETA and RICHARDSON fail to explicitly teach:
automatically modifying, by the one or more computing devices, based at least in part on the first composite result set, the quantum service definition file by replacing an instruction set of the one or more instruction sets with a different instruction set,
wherein a second composite result set associated with the different instruction set indicates a reduced impact on the one or more performance metrics of the plurality of quantum simulator nodes as compared to the instruction set of the one or more instruction sets.
However, in a related field of endeavor (enterprise software, see para. 0010), TANNIRU teaches:
automatically modifying, by the one or more computing devices, based at least in part on the first composite result set, the quantum service definition file by replacing an instruction set of the one or more instruction sets with a different instruction set, (TANNIRU, para. 0039: In some implementations, the one or more actions may include the intelligence platform automatically implementing a modification to code of the application based on the modified outputs. In this way, the intelligence platform may automatically implement the modification without requiring actions to be performed by one or more human resources of an entity associated with transitioning the application. This may save time and free entity personnel to perform other functions, thereby conserving resources of the entity associated with transitioning the application.”;
TANNIRU, para. 0088: “automatically implementing a modification to code of the application based on the modified outputs.”;
Examiner’s Note: TANNIRU discloses automatically modifying application code, based on a previous modified output; the KRNETA-RICHARDSON-TANNIRU combination now modifies the quantum simulators of KRNETA to perform multiple runs of each algorithm (as in RICHARDSON) and then takes the results from RICHARDSON and automatically updates the code in the file as taught by TANNIRU).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON and TANNIRU as explained above. As disclosed by TANNIRU, one of ordinary skill would have been motivated to do so in order to “save time and free entity personnel to perform other functions, thereby conserving resources.” (para. 0039).
However, KRNETA, RICHARDSON, and TANNIRU fail to explicitly teach:
wherein a second composite result set associated with the different instruction set indicates a reduced impact on the one or more performance metrics of the plurality of quantum simulator nodes as compared to the instruction set of the one or more instruction sets.
However, in a related field of endeavor (distributed computing resources over a network, see para. 0017), MANGIONE-SMITH teaches:
wherein a second composite result set associated with the different instruction set indicates a reduced impact on the one or more performance metrics of the plurality of quantum simulator nodes as compared to the instruction set of the one or more instruction sets. (MANGIONE-SMITH, para. 0016: “In one example, certain code segments of the virtual machine are modified, by way of example only, to automatically and dynamically change the domain in which the code segments execute. These modifications can reduce the number of context switches in the virtual machine and improve performance of the virtual machine itself and of the virtual machine environment.”;
MANGIONE-SMITH, para. 0047: “Application 522 may include an application program for providing access to physical resource 526 that is arranged to dynamically adapt code segments to run in different domains to improve performance of a virtual machine.”;
Examiner’s Note: MANGIONE-SMITH discloses automatically modifying code in a manner that reduces a number of context switches and improves performance (corresponding to recited “reduced impact on the one or more performance metrics ... as compared to the instruction set of the one or more instruction sets”; the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH combination now modifies the quantum simulators of KRNETA to perform multiple runs of each algorithm (as in RICHARDSON) and then takes the results from RICHARDSON and automatically updates the code in the file as taught by TANNIRU, and then runs another iteration using the updated code (where multiple iterations are taught by RICHARDSON at col. 22, lines 37-45) where such code updates reduce the impact on performance metrics as taught by MANGIONE-SMITH in comparison to the code prior to the updates).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON, TANNIRU, and MANGIONE-SMITH as explained above. As disclosed by MANGIONE-SMITH, one of ordinary skill would have been motivated to do so in order to reduce the negative impact of context switches that can negative impact performance. (para. 0016).
Regarding Claim 4
KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH teach the method of claim 1. KRNETA further teaches:
wherein for each instruction set, the result set obtained from each quantum simulator node is obtained based at least in part by obtaining data associated with a classical simulation of the quantum computing device configured in accordance with the parameter set associated with the quantum simulator node. (KRNETA, para. 0041: “For example, in some embodiments, user 116 may submit an algorithm to be simulated and quantum compute simulator using classical hardware 418 may determine resources needed to perform the simulation job, reserve the resources, configure the resources, etc. In some embodiments, quantum compute simulator using classical hardware 418 may include one or more “warm” simulators that are pre-configured simulators such that they are ready to perform a simulation job without a delay typically involved in reserving resources and configuring the resources to perform simulation.”; (EN): the pre-configured simulators, run on classical computing hardware, corresponds to the recited “classical simulation of the quantum computing device configured in accordance with the parameter set associated with the simulator node”)
Regarding Claim 5
KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH teach the method of claim 4. However, KRNETA fails to explicitly teach:
wherein for each instruction set, the result set obtained from each quantum simulator node is determined based at least in part on a key match associated with the instruction set at the quantum simulator node.
However, in a related field of endeavor (cloud-based simulation of quantum computing resources, see col. 5, lines 57-61), RICHARDSON teaches:
wherein for each instruction set, the result set obtained from each quantum simulator node is determined based at least in part on a key match associated with the instruction set at the quantum simulator node. (RICHARDSON, col. 20, lines 28-38: “In one embodiment, the computing resources may be selected based (at least in part) on performance metric analysis 630, e.g., on analysis of performance metrics associated with prior tasks. For example, if a similar task (e.g., a task related to the same quantum computing problem domain or using a similar quantum algorithm) previously had superior performance on one type of resource in comparison to another, then the computing resource selection component 610 may select the resource that had the superior performance.”; (EN): the examiner notes that the broadest reasonable interpretation of “key match associated with the instruction set” includes determining whether an instruction set “matches a key associated with an instruction set having a previously determined result”, and RICHARDSON teaches looking at similar quantum algorithms and their corresponding performance results when selecting resources; the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH combination now modifies the quantum simulators of KRNETA to search for previous performance of similar quantum algorithms as in RICHARDSON, e.g., when deciding which pre-configured simulator of KRNETA to use).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON, TANNIRU, and MANGIONE-SMITH as explained above. As further disclosed by RICHARDSON, one of ordinary skill would have been motivated to do so in order for “ the quantum computing simulation service 1500 may recommend a number of classical resources to a client, e.g., based on metrics for past simulations.” (col. 35, lines 32-35).
Regarding Claim 6
KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH teach the method of claim 1. However, KRNETA fails to explicitly teach:
wherein the first composite result set is representative of execution of the quantum service definition file across a plurality of configuration profiles for the quantum computing device.
However, in a related field of endeavor (cloud-based simulation of quantum computing resources, see col. 5, lines 57-61), RICHARDSON teaches:
wherein the first composite result set is representative of execution of the quantum service definition file across a plurality of configuration profiles for the quantum computing device. (RICHARDSON, col. 22, lines 38-42: “In one embodiment, the results aggregation 640 may collect and aggregate results of multiple runs of the same quantum algorithm on one or more quantum computing resources. The multiple runs may be performed in serial or in parallel.”; (EN): the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH combination now modifies the quantum simulators of KRNETA to run the same quantum algorithm on more than one quantum resource (e.g., on more than one of the pre-configured simulators of KRNETA) as disclosed by RICHARDSON).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON, TANNIRU, and MANGIONE-SMITH as explained above. As disclosed by RICHARDSON, one of ordinary skill would have been motivated to do so because averaging results over multiple runs increases the accuracy of a quantum simulation in view of the “probabilistic nature of quantum computing.” (col. 22, lines 37-45).
Regarding Claim 9
KRNETA teaches:
A computing device, comprising: a memory; and a processor device communicatively coupled to the memory to: (KRNETA, para. 0069: “FIG. 9 shows an example computing device to implement the various techniques described herein, according to some embodiments. For example, in one embodiment, the algorithm execution management system described above may be implemented by a computer device, for instance, a computer device as in FIG. 9 that includes one or more processors executing program instructions stored on a computer-readable storage medium coupled to the processors.”)
The remaining limitations correspond to the method of claim 1, and therefore this claim is rejected for the same reasons explained above with respect to claim 1.
Claim 12 depends from claim 9 and pertains to a computing device that corresponds to the method of claim 4, and is therefore rejected for the same reasons explained above with respect to claims 4 and 9.
Claim 13 depends from claim 12 and pertains to a computing device that corresponds to the method of claim 5, and is therefore rejected for the same reasons explained above with respect to claims 5 and 12.
Claim 14 depends from claim 9 and pertains to a computing device that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 9.
Regarding Claim 16
KRNETA teaches:
A non-transitory computer-readable storage medium having stored thereon computer-executable instructions that, when executed, cause one or more processor devices to: (KRNETA, para. 0069: “FIG. 9 shows an example computing device to implement the various techniques described herein, according to some embodiments. For example, in one embodiment, the algorithm execution management system described above may be implemented by a computer device, for instance, a computer device as in FIG. 9 that includes one or more processors executing program instructions stored on a computer-readable storage medium coupled to the processors.”)
The remaining limitations correspond to the method of claim 1, and therefore this claim is rejected for the same reasons explained above with respect to claim 1.
Claim 20 depends from claim 16 and pertains to a non-transitory computer-readable storage medium that corresponds to the method of claim 6, and is therefore rejected for the same reasons explained above with respect to claims 6 and 16.
Claims 2, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over KRNETA in view of RICHARDSON, TANNIRU, and MANGIONE-SMITH and further in view of US 20200401925 A1, hereinafter referenced as HERTZBERG.
Regarding Claim 2
KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH teach the method of claim 1. However, KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH fail to explicitly teach:
wherein the parameter set comprises data indicative of one or more of a number of qubits, qubit type, quantum error correction scheme, qubit load, or qubit noise profile.
However, in a related field of endeavor (quantum simulation, see para. 0003), HERTZBERG teaches:
wherein the parameter set comprises data indicative of one or more of a number of qubits, qubit type, quantum error correction scheme, qubit load, or qubit noise profile. (HERTZBERG, para. 0035: “In some embodiments, simulation component 108 can simulate operation of a quantum circuit and/or operation of one or more components of a quantum circuit (e.g., qubits, transmission lines, resonators, etc.). In some embodiments, simulation component 108 can simulate one or more parameters of a quantum circuit and/or one or more parameters of one or more components of a quantum circuit. For example, simulation component 108 can simulate one or more parameters including, but not limited to, quantum circuit architecture and/or topology parameters (e.g., quantity of qubits, location of qubits, qubit coupling parameters, etc.), frequency of components (e.g., qubit frequency, transmission line frequency, resonator frequency, etc.), component material (e.g., semiconducting and/or superconducting materials of quantum circuit such as, for instance, substrate material, materials of each qubit, transmission line material, resonator material, etc.), component dimensions (e.g., dimensions of quantum circuit and/or components thereof such as, dimensions of materials of each qubit, dimensions of transmission lines, dimensions of resonators, etc.), and/or another parameter.”; (EN): HERTZBERG teaches that parameters for quantum simulation include at least quantity of qubits (corresponding to recited “number of qubits”) and architecture and component material (corresponding to recited “qubit type”, e.g., different superconducting architectures); the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH-HERTZBERG combination now modifies the quantum simulator of KRNETA such that parameters including qubit type and qubit quantity are parameters that can be adjusted for the simulation; the examiner further notes that Fig. 4 of KRNETA shows at least 4 different types of quantum hardware providers (corresponding to different qubit types))
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON, TANNIRU, MANGIONE-SMITH, and HERTZBERG as explained above. As disclosed by HERTZBERG, one of ordinary skill would have been motivated to do so because HERTZBERG teaches techniques that “reduce the time and effort (e.g., human effort, computational effort and/or cost, etc.) needed to design and/or simulate one or more candidate quantum circuit topologies (e.g., superconducting circuit topologies) with proper accuracy.” (para. 0099). One of ordinary skill would further understand the benefit of a more granular quantum simulator that allows users to adjust parameters including qubit type and number of qubits, for example, to simulate a particular hardware arrangement for a particular quantum algorithm.
Claim 10 depends from claim 9 and pertains to a computing device that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 9.
Claim 17 depends from claim 16 and pertains to a non-transitory computer-readable storage medium that corresponds to the method of claim 2, and is therefore rejected for the same reasons explained above with respect to claims 2 and 16.
Claims 3, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over KRNETA in view of RICHARDSON, TANNIRU, and MANGIONE-SMITH and further in view of US 20210216898 A1, hereinafter referenced as HUFFMAN.
Regarding Claim 3
KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH teach the method of claim 1. However, KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH fail to explicitly teach:
wherein the one or more performance metrics comprise data indicative of one or more of operating temperature, qubit availability, processor resource consumption, qubit resource consumption, qubit relaxation time, or qubit coherence time.
However, in a related field of endeavor (quantum circuit simulation, see para. 0003), HUFFMAN teaches:
wherein the one or more performance metrics comprise data indicative of one or more of operating temperature, qubit availability, processor resource consumption, qubit resource consumption, qubit relaxation time, or qubit coherence time. (HUFFMAN, para. 0026: “The system 100 can further include a system bus 106 that can couple various components including, but not limited to: (1) a simulation component 108 that simulates a quantum circuit; ... (3) a determination component 112 that sets a desired threshold for percentage of qubit relaxation (T1) across the quantum circuit; (4) a receiving component 124 that receives the compiled quantum circuit from a classical computer; (5) a computing component 126 that can employ a qubit's T1 values and multiply it times the percent relaxation set to generate a new threshold for a qubit; (6) a transmitting component 128 that transmits the computed circuit to the classical computer; and (7) a visualization component 114 that generates a visualization of the qubit relaxation (T1) by altering visual appearance of a qubit based on the qubit relaxation (T1).”; (EN): HUFFMAN teaches simulating and measuring performance with respect to qubit relaxation time; the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH-HUFFMAN combination now modifies the quantum simulator of KRNETA such that parameters including qubit relaxation time can be adjusted for the simulation and measured as a performance metric)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON, TANNIRU, MANGIONE-SMITH, and HUFFMAN as explained above. As disclosed by HUFFMAN, one of ordinary skill would have been motivated to do so because HUFFMAN teaches techniques that “provide a unique methodology in quantum computing to understand how noise and error implicate circuit results. By visualizing qubit relaxation (T1), a better understanding can be provided of how to optimize a quantum circuit to represent and account for errors caused by qubit relaxation.” (para. 0023). One of ordinary skill would further understand the benefit of a more granular quantum simulator that allows users to adjust parameters including qubit relaxation time, for example, to simulate a particular hardware arrangement for a particular quantum algorithm.
Claim 11 depends from claim 9 and pertains to a computing device that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 9.
Claim 18 depends from claim 16 and pertains to a non-transitory computer-readable storage medium that corresponds to the method of claim 3, and is therefore rejected for the same reasons explained above with respect to claims 3 and 16.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over KRNETA in view of RICHARDSON and TANNIRU and MANGIONE-SMITH and further in view of US 20200117764 A1, hereinafter referenced as ZUCCARELLI.
Regarding Claim 7
KRNETA and RICHARDSON teach the method of claim 1 (including the “wherein determining, by the one or more computing devices, a first composite result set based at least in part on the result set from each quantum simulator node” step of claim 1). KRNETA further teaches:
storing, by the one or more computing devices, the result set from each quantum simulator node in memory; (KRNETA, para. 0032: “ In some embodiments, the results may be stored in one or more data stores of a data storage service of provider network 102.”)
However, KRNETA, RICHARDSON, TANNIRU, and MANGIONE-SMITH fail to explicitly teach:
performing, by the one or more computing devices, a map function on the result set stored in the memory; and
performing, by the one or more computing devices, a reduce function on the result set stored in the memory.
However, in a related field of endeavor (“validating and optimizing a quantum computing simulator”, see para. 0001), ZUCCARELLI teaches:
performing, by the one or more computing devices, a map function on the result set stored in the memory; (ZUCCARELLI, para. 0073: “ In addition to repackaging and reformatting, the output files may also be mapped and reduced by a MapReduce function. A MapReduce function is a combination of a map function, which maps a set of data into tuples or key/value pairs, and a reduce function, which combines the mapped tuples into another data set and selects one or more of the mapped tuples belonging to the same subset of tuples as a result. For example, the output file of the quantum computing simulator 408A containing the formatted first result 418 may be mapped and reduced to generate a map reduced first result 422.”; (EN): the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH-ZUCCARELLI combination now modifies the quantum simulator of KRNETA to perform a mapping function on the results of KRNETA as in ZUCCARELLI)
performing, by the one or more computing devices, a reduce function on the result set stored in the memory. (ZUCCARELLI, para. 0073: “ In addition to repackaging and reformatting, the output files may also be mapped and reduced by a MapReduce function. A MapReduce function is a combination of a map function, which maps a set of data into tuples or key/value pairs, and a reduce function, which combines the mapped tuples into another data set and selects one or more of the mapped tuples belonging to the same subset of tuples as a result. For example, the output file of the quantum computing simulator 408A containing the formatted first result 418 may be mapped and reduced to generate a map reduced first result 422.”; (EN): the KRNETA-RICHARDSON-TANNIRU-MANGIONE-SMITH-ZUCCARELLI combination now modifies the quantum simulator of KRNETA to perform a reduce function on the results of KRNETA as in ZUCCARELLI)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to have combined the quantum simulation teachings of KRNETA with the teachings of RICHARDSON, TANNIRU, MANGIONE-SMITH, and ZUCCARELLI as explained above. As disclosed by ZUCCARELLI, one of ordinary skill would have been motivated to do so because ZUCCARELLI teaches that “MapReduce may be used to reduce the number of tuples for comparison to a more manageable number” with respect to simulating tuples in a quantum computer. (para. 0075).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20090204939 A1 (Lavrova). “A key part of the present invention is for investigating the sources or evaluating the sources first, before or while evaluating edits to code to understand the impact of a change. The present invention provides first for investigation, for research to get the structure so that the reviewer is provided automatically with information about what happens if one variable is changed, to make sure that nothing else is impacted, or at least whether it is impacted negatively or how it is impacted.” (para. 0023).
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/MICHAEL C. LEE/Examiner, Art Unit 2128