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 .
Response to Amendment
Applicant’s response dated 3/16/2026 has been considered. Claims 1-7, 9-17, and 19-20 are pending. No claim amendments were made in the 3/16/2026 submission.
Response to Arguments
On page 9 of Applicant’s 3/16/2025 Response, with respect to the rejection of claim 1 under 35 U.S.C. 103 as obvious in view of the THOMPSON, KRNETA, and RIGETTI references, Applicant argues that RIGETTI does not teach the “... based on the first simulation results, wherein the first simulation results describe a first value for an operating condition for the optimal qubit type and a second value for the operating condition for a different qubit type of the plurality of different qubit types, wherein the first value is greater than the second value” limitation.
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In response to Applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). As explained on pages 10-12 of the 12/16/2025 Office Action, it is the combination of the THOMPSON, KRNETA, and RIGETTI references that teaches this limitation.
THOMPSON teaches the recited “first simulation results” based on execution of the instructions by a quantum computer simulator. (THOMPSON, para. 0120).
KRNETA teaches a quantum hardware recommendation/selection module that selects from a plurality of qubit types (“superconductors, trapped ions, semiconductors, photonics”) to recommend a certain qubit type for a user’s quantum algorithm (corresponding to recited “optimal qubit type” and “different qubit type” limitations). (KRNETA, paras. 0015, 0020, 0042).
RIGETTI discloses simulating different operating parameters for one or more qubit devices and using the results of the simulated operating parameters to modify a circuit specification. (RIGETTI, paras. 0046, 0047, 0117). In particular, RIGETTI teaches that “based on the simulated operating parameters for the current iteration, modifying the circuit specification for the next iteration of the feedback process.” (RIGETTI, para. 0117).
The THOMPSON-KRNETA-RIGETTI combination now modifies the quantum computer type selection of THOMPSON (see para. 0042 of THOMPSON for selection between different types of quantum computers), to utilize the quantum hardware recommendation/selection module of KRNETA that recommends a type of computer based on the algorithm, where the recommendation/selection module of KRNETA now utilizes the simulation results that determine simulated operating parameters for the different types of qubit types (e.g., annealing QHP, ion trap QHP, superconducting QHP, and/or photon-based QHP) as in RIGETTI, where one of ordinary skill would understand that different qubit types will have non-equal operating parameter values for the same operating parameter (corresponding to recited “wherein the first value is greater than the second value” limitation)). In other words, the teachings of RIGETTI with respect to iterative simulations, and making modifications for future iterations based on current simulation results, is used together with THOMPSON and KRNETA to iteratively simulate a quantum algorithm and recommend a qubit type, where current simulation results inform the recommended qubit type for the next iteration of the algorithm, such that an operating parameter for the simulated iteration has a higher value than for a different iteration with a different qubit type.
On page 9 of Applicant’s 3/16/2025 Response, with respect to the rejection of claim 1 under 35 U.S.C. 103 as obvious in view of the THOMPSON, KRNETA, and RIGETTI references, Applicant argues:
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The examiner respectfully disagrees. First, the office action does not “evaluate the elements in isolation” as Applicant alleges. As explained above, the office action explains how the combination of the THOMPSON, KRNETA, and RIGETTI teaches the limitation “as a whole”. The office action explained, in detail, how one of ordinary skill in the art would utilize the various teachings of THOMPSON, KRNETA, and RIGETTI together.
On page 10 of Applicant’s 3/16/2025 Response, with respect to the rejection of claim 1 under 35 U.S.C. 103 as obvious in view of the THOMPSON, KRNETA, and RIGETTI references, Applicant argues:
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The examiner respectfully disagrees. The Office Action does not “assert that Rigetti disclosing ‘the first simulation results’ would somehow grant the ‘recommendation/selection module’ of Krneta the ability to use the ‘first simulation results’ when making a recommendation.” Applicant’s response ignores the actual combination explained in the office action. RIGETTI teaches iteratively making updates to operating parameters based on simulation results, and together with THOMPSON and KRNETA, modifies the quantum simulator of THOMPSON to utilize the qubit type recommender/selector of KRNETA, to iteratively simulate results to improve the recommended qubit type and simulation as a whole.
On page 10 of Applicant’s 3/16/2025 Response, Applicant argues that independent claims 17 and 20, and all dependent claims, should be allowed for the same reasons argued with respect to claim 1.
The examiner respectfully disagrees for the same reasons explained with respect to claim 1.
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 1-7, 10, 12-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210398007 A1, hereinafter referenced as THOMPSON in view of US 20230153155 A1, hereinafter referenced as KRNETA, and further in view of US 20170228483 A1, hereinafter referenced as RIGETTI.
Regarding Claim 1
THOMPSON teaches:
A method, comprising: (THOMPSON, para. 0004: “The present disclosure relates generally to an improved computer system and, in particular, to a method, apparatus, system, and computer program product for managing an execution of quantum processes for quantum computers.”)
receiving, by a classical computing system comprising one or more processor devices, a first quantum service request (THOMPSON, para. 0035 and Fig. 1: “process manager 130 is located in server computer 104. As depicted, process manager 130 can operate to manage processes. For example, process manager 130 can manage the running of quantum process 132 on quantum computer 112 and quantum process 134 running on quantum computer 114.”; Examiner’s Note (EN): server computer 104 corresponds to the recited “classical computing system” having at least one processor, and the quantum process is received by process manager 130 on the server 104, where the quantum process corresponds to the recited “first quantum service request”) executable by a quantum computing system comprising a plurality of quantum computing devices, (THOMPSON, para. 0032 and Fig. 1: “In this illustrative example, quantum computer 112 is a superconducting quantum computer while quantum computer 114 is an ion trap quantum computer.”; (EN): superconducting and trap ion are 2 different types of actual quantum computing systems) each quantum computing device of the plurality of quantum computing devices comprising a plurality of qubits of a qubit type; (THOMPSON, para. 0074: “Each gate defined in gate models 400 can perform an operation on one or more qubits.”
THOMPSON, para. 0080: “qubit model 402 defines qubits 416 used in a quantum computer. A qubit is a basic unit of information in the quantum computer and can take different forms.”;
THOMPSON, para. 0081: “In this illustrative example, qubit model 402 can define one or more types of qubits 416 that may be used within a quantum computer. For example, qubit model 402 can include qubit types for qubits 416 selected from at least one an idealized qubit, an ion trap qubit, a neutral atom qubit, a superconducting qubit, an electron spin qubit, a photon polarization qubit, a dot spin qubit, or some other suitable type of qubit.”)
providing, by the classical computing system, the first quantum service request to each of a plurality of simulator processes executing on the classical computing system for a simulated execution of the first quantum service request, (THOMPSON, para. 0035 and Fig. 1: “process manager 130 is located in server computer 104. As depicted, process manager 130 can operate to manage processes. For example, process manager 130 can manage the running of quantum process 132 on quantum computer 112 and quantum process 134 running on quantum computer 114.”;
THOMPSON, para. 0036: “quantum process 132 and quantum process 134 are comprised of instructions in one or more quantum processing languages. As depicted, instructions 136 for quantum process 132 can be in a different quantum programming language from instructions 138 for quantum process 134.”;
THOMPSON, para. 0042: “In this illustrative example, quantum computers 202 in quantum computing environment 200 can each run process 204.
THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation.”
Examiner’s Note: process manager 130 (running on server 104, which is a classical computing system), provides quantum process 132/134 (first quantum service request, where the difference is that they are in a different language depending on the quantum platform), to each of simulated quantum computers 112 and 114) each simulator process of the plurality of simulator processes based on a hardware profile of one of the plurality of quantum computing devices, the hardware profile comprising the qubit type of the plurality of qubits; and (THOMPSON, para. 0032 and Fig. 1: “In this illustrative example, quantum computer 112 is a superconducting quantum computer while quantum computer 114 is an ion trap quantum computer.”;
THOMPSON, para. 0081: “In this illustrative example, qubit model 402 can define one or more types of qubits 416 that may be used within a quantum computer. For example, qubit model 402 can include qubit types for qubits 416 selected from at least one an idealized qubit, an ion trap qubit, a neutral atom qubit, a superconducting qubit, an electron spin qubit, a photon polarization qubit, a dot spin qubit, or some other suitable type of qubit.”;
THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation.”
Examiner’s Note: superconducting quantum computer 112 is simulated using superconducting qubits and ion trap quantum computer is simulated using ion trap qubits)
receiving, by the classical computing system from each simulator process of the plurality of simulator processes, first simulation results of execution of the first quantum service request; and (THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation. The process receives results from an execution of the instructions on the quantum computer (operation 920). The process saves the results (operation 922).”)
However, THOMPSON fails to explicitly teach:
determining, by the classical computing system, an optimal qubit type of a plurality of different qubit types that optimizes execution of the first quantum service request based on the first simulation results, wherein the first simulation results describe a first value for an operating condition for the optimal qubit type and a second value for the operating condition for a different qubit type of the plurality of different qubit types, wherein the first value is greater than the second value
However, in a related field of endeavor (“an algorithm execution management system of a provider network” with respect to quantum computing resources, see para. 0014), KRNETA teaches:
determining, by the classical computing system, an optimal qubit type of a plurality of different qubit types that optimizes execution of the first quantum service request ... (KRNETA, para. 0015: “based on the algorithm provided by the user, the algorithm execution management system may identify or select appropriate quantum computing resources (e.g., from a pool of quantum computing resources) for the user.”;
KRNETA, para. 0020: “By comparison, quantum computing resources 112 may include various quantum computers, quantum processing units (QPUs), and/or quantum hardware based on qubits. In some embodiments, quantum computing resources 112 may be implemented using qubits built from superconductors, trapped ions, semiconductors, photonics, etc.”;
KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
Examiner’s Note (EN): KRNETA teaches a quantum hardware recommendation/selection module that selects from a plurality of qubit types (“superconductors, trapped ions, semiconductors, photonics”), where such selection is based on specific quantum algorithm provided by a user; the THOMPSON-KRNETA combination now modifies the quantum computer type selection of THOMPSON (see para. 0042 of THOMPSON for selection between different types of quantum computers), to utilize the quantum hardware recommendation/selection module of KRNETA that recommends a type of computer based on the algorithm (corresponding to recited “first quantum service request”))
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
However, THOMPSON and KRNETA fail to explicitly teach:
... based on the first simulation results, wherein the first simulation results describe a first value for an operating condition for the optimal qubit type and a second value for the operating condition for a different qubit type of the plurality of different qubit types, wherein the first value is greater than the second value
However, in a related field of endeavor (determining simulated operating parameters of a quantum information processing circuit, see para. 0005), RIGETTI teaches:
... based on the first simulation results, wherein the first simulation results describe a first value for an operating condition for the optimal qubit type and a second value for the operating condition for a different qubit type of the plurality of different qubit types, wherein the first value is greater than the second value (RIGETTI, para. 0046:” In some implementations, the quantum circuit analysis tool 213 computes the simulated operating parameters 225 by executing a quantum simulation algorithm.”;
RIGETTI, para. 0047: “The simulated operating parameters 225 can include, for example, a coherence time of a qubit device in the quantum information processing circuit, a resonance frequency of a qubit device in the quantum information processing circuit, a coupling strength between devices in the quantum information processing circuit, or a combination of these and other operating parameters. For instance, any of the example operating parameters shown in table 470 in FIG. 4D may be determined for one or more qubit devices, readout devices or other types of devices in a quantum information processing circuit.”;
RIGETTI, para. 0117: “determining simulated operating parameters for the current iteration based on the linear response function for the current iteration; and based on the simulated operating parameters for the current iteration, modifying the current circuit specification for the next iteration of the feedback process”;
Examiner’s Note: RIGETTI discloses simulating different operating parameters for one or more qubit devices and using the results of the simulated operating parameters to modify a circuit specification; the THOMPSON-KRNETA-RIGETTI combination now modifies the quantum computer type selection of THOMPSON (see para. 0042 of THOMPSON for selection between different types of quantum computers), to utilize the quantum hardware recommendation/selection module of KRNETA that recommends a type of computer based on the algorithm, where the recommendation/selection module of KRNETA now utilizes the simulation results that determine simulated operating parameters for the different types of qubit types (e.g., annealing QHP, ion trap QHP, superconducting QHP, and/or photon-based QHP) to as in RIGETTI, where one of ordinary skill would understand that different qubit types will have non-equal operating parameter values for the same operating parameter (corresponding to recited “wherein the first value is greater than the second value” limitation)).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices. As disclosed by RIGETTI, one of ordinary skill would have been motivated to do so because RIGETTI teaches performing quantum circuit analyses in order to “analyze loss mechanisms in superconducting qubits, to design and more accurately simulate complex quantum information processing circuits, to analyze various types of quantum mechanical elements (e.g., various types of qubits, quantum limited amplifiers, etc.) or for other purposes.” (para. 0006). As further disclosed by RIGETTI, one of ordinary skill would have been motivated to do so because RIGETTI teaches that the simulated operating parameters can be used to check to see if a design meets “performance requirements or other criteria.” (para. 0048).
Regarding Claim 2
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. THOMPSON further teaches:
receiving, by the classical computing system, hardware information from a quantum computing device of the plurality of quantum computing devices, the hardware information comprising the qubit type of the plurality of qubits of the quantum computing device; (THOMPSON, para. 0039: process manager 130 utilizes digital model representations 140 of hardware components arranged to perform the operations for processes. In this illustrative example, process manager 130 can use translator system 142 to translate a digital model representation in digital model representations 140 for a process into instructions 136 and instructions 138. In this manner, a single process can be turned into instructions for execution on different hardware systems such as quantum computer 112 and quantum computer 114 that may be of different computer types.”;
THOMPSON, para. 0042: “In this illustrative example, quantum computers 202 in quantum computing environment 200 can each run process 204. Quantum computers 202 can take a number of different forms. For example, quantum computers 202 can have computer types 203 that are the same or different from each other. In other words, a quantum computer in quantum computers 202 can have at least one of different physical hardware, architecture, or other features that may have constraints as to how operations can be performed as compared to other quantum computers in quantum computers 202 of different ones of computer types 203. In the illustrative example, computer types 203 can be selected from at least one of a superconducting quantum computer, an ion trap quantum computer, a topological quantum computer, a quantum dot quantum computer, an optical lattice quantum computer, a cavity quantum electrodynamic quantum computer, a nuclear magnetic resonance quantum computer, a nitrogen vacancy diamond quantum computer, a hybrid quantum computer combining one or more types of quantum computers, or some other type of quantum computer.”; (EN): process manager 130 receives (and accesses) the digital model representation of a computer type (corresponding to hardware information) in order to determine which model to use for simulation, for example, where superconducting quantum computers use superconducting qubits and trap ion quantum computers use trap ion qubits as explained in para. 0081; see also Fig. 2, showing gate models 410)
generating, by the classical computing system, the hardware profile based on the hardware information; and (THOMPSON, para. 0039: process manager 130 utilizes digital model representations 140 of hardware components arranged to perform the operations for processes. In this illustrative example, process manager 130 can use translator system 142 to translate a digital model representation in digital model representations 140 for a process into instructions 136 and instructions 138. In this manner, a single process can be turned into instructions for execution on different hardware systems such as quantum computer 112 and quantum computer 114 that may be of different computer types.”;
THOMPSON, para. 0062: “For example, digital model representation 226 of quantum computer components 228 can be created from user input 236 generated by human operator 230 interacting with human machine interface 240.”;
Examiner’s Note: digital model representations are created by humans using a classical computer).
storing, by the classical computing system, the hardware profile in a quantum hardware profile repository. (THOMPSON, para. 0039: “process manager 130 utilizes digital model representations 140 of hardware components arranged to perform the operations for processes.”;
THOMPSON, para.0048: “digital model representation 226 is a data structure used by process manager 214”
Examiner’s Note: as depicted in Fig. 1, digital model representations 140 are stored on server computer 104; see also Fig. 2, showing that computer system 208 includes a data structure for the digital model representations, corresponding to the recited “quantum hardware profile repository”)
Regarding Claim 3
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 2. THOMPSON further teaches:
receiving, by the classical computing system, the hardware information from the quantum computing device, the hardware information comprising the qubit type of the plurality of qubits of the quantum computing device, the qubit type comprising photon, coherent state of light, electron, nucleus, optical lattices, Josephson junction, singly charged quantum dot pair, quantum dot, Gapped topological system, or van der Waals heterostructure. (THOMPSON, para. 0081: “In this illustrative example, qubit model 402 can define one or more types of qubits 416 that may be used within a quantum computer. For example, qubit model 402 can include qubit types for qubits 416 selected from at least one an idealized qubit, an ion trap qubit, a neutral atom qubit, a superconducting qubit, an electron spin qubit, a photon polarization qubit, a dot spin qubit, or some other suitable type of qubit.”; (EN): photon polarization qubit corresponds to recited “photon” qubit and the dot spin qubit corresponds to the “singly charged quantum dot pair”)
Regarding Claim 4
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 2. THOMPSON further teaches:
receiving, by the classical computing system, the hardware information from the quantum computing device, the hardware information comprising the qubit type of the plurality of qubits of the quantum computing device and at least one of a temperature, noise, error rate, last time rebooted, or hardware load. (THOMPSON, para. 0060: “as a result, universal gate set 248 can be selected by process manager 214 using hardware database 249 to provide a desired level of performance for a particular quantum computer. The performance can be selected from at least one of accuracy, speed, hardware support, circuit depth, noise or error rates, gate performance, or other performance factors.”)
Regarding Claim 5
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. THOMPSON further teaches:
inputting the first quantum service request into each of the plurality of simulator processes, the first quantum service request comprising a QASM (quantum assembly language) file. (THOMPSON, para. 0085: “Turning next to FIG. 5, an illustration of a diagram of a generation of instructions in a target quantum programming language is depicted in accordance with an illustrative embodiment. In this illustrative example, instructions 500 are lines of code in quantum assembly language (QASM).”
THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation. The process receives results from an execution of the instructions on the quantum computer (operation 920). The process saves the results (operation 922).”)
Regarding Claim 6
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. THOMPSON further teaches:
receiving, by the classical computing system from each simulator process of the plurality of simulator processes, the first simulation results of execution of the first quantum service request, the first simulation results comprising at least one of execution time, number of errors, or system impact. (THOMPSON, para. 0109: “For example, these operations can be performed for a benchmarking process in which instructions for the same process are run on different quantum computers. When the process is part of a benchmarking process, the instructions can be for benchmarking tests. The different parameters for a process can include at least one of processor execution time, memory used, processor resources used, accuracy compared to a known solution, probability of a correct solution compared to a known solution, error, noise, circuit execution time, or other suitable parameters.”; (EN): THOMPSON teaches the execution time and error parameters can be used to benchmark the different quantum computers for each process)
Regarding Claim 7
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. THOMPSON further teaches:
forwarding, by the classical computing system, the first simulation results of each of the plurality of simulator processes to a user computing device. (THOMPSON, para. 0025: “In the depicted example, server computer 104 provides information, such as boot files, operating system images, and applications to client devices 110. Further, client devices 110 can also include other types of client devices such as mobile phone 118, tablet computer 120, and smart glasses 122. In this illustrative example, server computer 104, server computer 106, storage unit 108, and client devices 110 are network devices that connect to network 102 in which network 102 is the communications media for these network devices. Some or all of client devices 110 may form an Internet-of-things (IoT) in which these physical devices can connect to network 102 and exchange information with each other over network 102.”;
THOMPSON, para;. 0064: “results of simulation 254 can be displayed through human machine interface 240 as simulation 254 is performed as well as when simulation 254 is completed”;
Examiner’s Note: simulation results are displayed to a human operator, who can be using a user computer device such as a mobile phone, tablet computer, smart glasses, or any other user device acting as a human-machine interface for the simulator)
Regarding Claim 10
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. However, THOMPSON fails to explicitly teach:
determining, by the classical computing system, from a plurality of quantum computing devices comprising the optimal qubit type, an optimal quantum computing device to optimize execution of the first quantum service request.
However, in a related field of endeavor (“an algorithm execution management system of a provider network” with respect to quantum computing resources, see para. 0014), KRNETA teaches:
determining, by the classical computing system, from a plurality of quantum computing devices comprising the optimal qubit type, an optimal quantum computing device to optimize execution of the first quantum service request. (KRNETA, para. 0015: “based on the algorithm provided by the user, the algorithm execution management system may identify or select appropriate quantum computing resources (e.g., from a pool of quantum computing resources) for the user.”;
KRNETA, para. 0020: “By comparison, quantum computing resources 112 may include various quantum computers, quantum processing units (QPUs), and/or quantum hardware based on qubits. In some embodiments, quantum computing resources 112 may be implemented using qubits built from superconductors, trapped ions, semiconductors, photonics, etc.”;
KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
Examiner’s Note (EN): KRNETA teaches a quantum hardware recommendation/selection module that selects a specific quantum computing resource from a plurality of qubit types (“superconductors, trapped ions, semiconductors, photonics”), where such selection is based on specific quantum algorithm provided by a user; the THOMPSON-KRNETA-RIGETTI combination now modifies the quantum computer type selection of THOMPSON (see para. 0042 of THOMPSON for selection between different types of quantum computers), to utilize the quantum hardware recommendation/selection module of KRNETA that recommends a specific quantum computing resource of a particular type of quantum computer based on the algorithm (corresponding to recited “first quantum service request”))
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
Regarding Claim 12
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. THOMPSON and KRNETA further make obvious:
determining the optimal qubit type based on at least one of fastest execution time, fewest errors, minimized system impact, or minimized environmental impact. (THOMPSON, para. 0109: “When the process is part of a benchmarking process, the instructions can be for benchmarking tests. The different parameters for a process can include at least one of processor execution time, memory used, processor resources used, accuracy compared to a known solution, probability of a correct solution compared to a known solution, error, noise, circuit execution time, or other suitable parameters.”
KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
Examiner Note: in combination with the benchmark comparison of 2 or more quantum computers as explained in THOMPSON, where benchmark parameters include processor execution time, memory used, accuracy, error, noise, circuit execution time, etc. (see paras. 0033, 0109), THOMPSON now benchmarks the different quantum computers and then selects the optimal quantum computing resources as in KRNETA.
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
Regarding Claim 13
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. THOMPSON further teaches:
receiving, by the classical computing system, a second quantum service request for execution by the quantum computing system; (THOMPSON, para. 0035 and Fig. 1: “process manager 130 is located in server computer 104. As depicted, process manager 130 can operate to manage processes. For example, process manager 130 can manage the running of quantum process 132 on quantum computer 112 and quantum process 134 running on quantum computer 114.”; Examiner’s Note (EN): server computer 104 corresponds to the recited “classical computing system”, and the second (or any number of) quantum process is received by process manager 130 on the server 104, where the quantum process corresponds to the recited “first quantum service request”)
causing, by the classical computing system, execution of the second quantum service request by each of the plurality of simulator processes; and (THOMPSON, para. 0035 and Fig. 1: “process manager 130 is located in server computer 104. As depicted, process manager 130 can operate to manage processes. For example, process manager 130 can manage the running of quantum process 132 on quantum computer 112 and quantum process 134 running on quantum computer 114.”;
THOMPSON, para. 0036: “quantum process 132 and quantum process 134 are comprised of instructions in one or more quantum processing languages. As depicted, instructions 136 for quantum process 132 can be in a different quantum programming language from instructions 138 for quantum process 134.”;
THOMPSON, para. 0042: “In this illustrative example, quantum computers 202 in quantum computing environment 200 can each run process 204.
THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation.”
Examiner’s Note: process manager 130 (running on server 104, which is a classical computing system), provides quantum process 132/134 (second quantum service request, where the difference is that they are in a different language depending on the quantum platform), to each of simulated quantum computers 112 and 114)
receiving, by the classical computing system from each simulator process of the plurality of simulator processes, second simulation results of execution of the second quantum service request. (THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation. The process receives results from an execution of the instructions on the quantum computer (operation 920). The process saves the results (operation 922).”)
Regarding Claim 14
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 13. However, THOMPSON fails to explicitly teach:
determining, by the classical computing system, a first optimal qubit type to optimize execution of the first quantum service request and a second optimal qubit type to optimize execution of the second quantum service request.
However, in a related field of endeavor (configuring quantum circuitry within a quantum computer, see para. 0001), KRNETA teaches:
determining, by the classical computing system, a first optimal qubit type to optimize execution of the first quantum service request and a second optimal qubit type to optimize execution of the second quantum service request. (KRNETA, para. 0015: “based on the algorithm provided by the user, the algorithm execution management system may identify or select appropriate quantum computing resources (e.g., from a pool of quantum computing resources) for the user.”;
KRNETA, para. 0020: “By comparison, quantum computing resources 112 may include various quantum computers, quantum processing units (QPUs), and/or quantum hardware based on qubits. In some embodiments, quantum computing resources 112 may be implemented using qubits built from superconductors, trapped ions, semiconductors, photonics, etc.”;
KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
Examiner’s Note (EN): KRNETA teaches a quantum hardware recommendation/selection module that selects from a plurality of qubit types (“superconductors, trapped ions, semiconductors, photonics”), where such selection is based on specific quantum algorithm provided by a user; the THOMPSON-KRNETA-RIGETTI combination now modifies the quantum computer type selection of THOMPSON (see para. 0042 of THOMPSON for selection between different types of quantum computers), to utilize the quantum hardware recommendation/selection module of KRNETA that recommends a type of computer based on the algorithm (corresponding to recited “first quantum service request” and “second quantum service request”), and can do so for both the recited first and second quantum service requests; the examiner further notes that pursuant to MPEP 2144.04 VI, duplicating the same steps for the first and second quantum services request has “no patentable significance unless a new and unexpected result is produced.”)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
Regarding Claim 15
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 13. However, THOMPSON fails to explicitly teach:
determining, by the classical computing system, from the plurality of quantum computing devices, a first optimal quantum computing device to execute the first quantum service request and a second optimal quantum computing device to execute the second quantum service request to optimize execution by a quantum computing system.
However, in a related field of endeavor (“an algorithm execution management system of a provider network” with respect to quantum computing resources, see para. 0014), KRNETA teaches:
determining, by the classical computing system, from the plurality of quantum computing devices, a first optimal quantum computing device to execute the first quantum service request and a second optimal quantum computing device to execute the second quantum service request to optimize execution by a quantum computing system. (KRNETA, para. 0015: “based on the algorithm provided by the user, the algorithm execution management system may identify or select appropriate quantum computing resources (e.g., from a pool of quantum computing resources) for the user.”;
KRNETA, para. 0020: “By comparison, quantum computing resources 112 may include various quantum computers, quantum processing units (QPUs), and/or quantum hardware based on qubits. In some embodiments, quantum computing resources 112 may be implemented using qubits built from superconductors, trapped ions, semiconductors, photonics, etc.”;
KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
Examiner’s Note (EN): in combination with the benchmark comparison of 2 or more quantum computers as explained in THOMPSON, where benchmark parameters include processor execution time, memory used, accuracy, error, noise, circuit execution time, etc. (see paras. 0033, 0109), THOMPSON now benchmarks the different quantum computers and then selects the optimal quantum computing resource as in KRNETA; the examiner further notes that pursuant to MPEP 2144.04 VI, duplicating the same steps for the first and second quantum services request has “no patentable significance unless a new and unexpected result is produced.”)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
Regarding Claim 16
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 15. THOMPSON and KRNETA further make obvious:
determining, by the classical computing system, from the plurality of quantum computing devices, the first optimal quantum computing device to execute the first quantum service request and the second optimal quantum computing device to execute the second quantum service request to optimize execution by the quantum computing system, (KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
(EN): the THOMPSON-KRNETA-RIGETTI combination now has the classical computer system of THOMPSON (such as server 104), utilize the quantum hardware recommendation/selection module of KRNETA in order to determine the optimal qubit type and optimal quantum computing system, using the benchmarks of THOMPSON)
wherein optimizing execution by the quantum computing system comprises at least one of fastest execution time, fewest errors, minimized system impact, or minimized environmental impact across the quantum computing system. (THOMPSON, para. 0109: “When the process is part of a benchmarking process, the instructions can be for benchmarking tests. The different parameters for a process can include at least one of processor execution time, memory used, processor resources used, accuracy compared to a known solution, probability of a correct solution compared to a known solution, error, noise, circuit execution time, or other suitable parameters.”; (EN): the THOMPSON-KRNETA-RIGETTI combination now has the classical computer system of THOMPSON (such as server 104), utilize the quantum hardware recommendation/selection module of KRNETA in order to determine the optimal qubit type and optimal quantum computing system, using the benchmarks of THOMPSON that relate to execution time and errors)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
Regarding Claim 17
THOMPSON teaches:
A classical computing system comprising: (THOMPSON, para. 0124: “Turning now to FIG. 10, an illustration of a block diagram of a data processing system is depicted in accordance with an illustrative embodiment. Data processing system 1000 can be used to implement server computer 104, server computer 106, client devices 110, in FIG. 1.”)
a processor device; (THOMPSON, para. 0125: “Processor unit 1004 serves to execute instructions for software that can be loaded into memory 1006. Processor unit 1004 includes one or more processors.”)
The remaining limitations in claim 17 correspond to the method of claim 1, and therefore claim 17 is rejected for the same reasons explained above with respect to claim 1 under 35 U.S.C. 103 in view of the THOMPSON, KRNETA, and RIGETTI references.
Regarding Claim 19
THOMPSON, KRNETA, and RIGETTI teach the classical computing system of claim 17. THOMPSON further teaches:
receive a second quantum service request for execution by the quantum computing system; (THOMPSON, para. 0035 and Fig. 1: “process manager 130 is located in server computer 104. As depicted, process manager 130 can operate to manage processes. For example, process manager 130 can manage the running of quantum process 132 on quantum computer 112 and quantum process 134 running on quantum computer 114.”; Examiner’s Note (EN): server computer 104 corresponds to the recited “classical computing system”, and the second (or any number of) quantum process is received by process manager 130 on the server 104, where the quantum process corresponds to the recited “first quantum service request”)
cause execution of the second quantum service request by each of the plurality of simulator processes; (THOMPSON, para. 0035 and Fig. 1: “process manager 130 is located in server computer 104. As depicted, process manager 130 can operate to manage processes. For example, process manager 130 can manage the running of quantum process 132 on quantum computer 112 and quantum process 134 running on quantum computer 114.”;
THOMPSON, para. 0036: “quantum process 132 and quantum process 134 are comprised of instructions in one or more quantum processing languages. As depicted, instructions 136 for quantum process 132 can be in a different quantum programming language from instructions 138 for quantum process 134.”;
THOMPSON, para. 0042: “In this illustrative example, quantum computers 202 in quantum computing environment 200 can each run process 204.
THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation.”
Examiner’s Note: process manager 130 (running on server 104, which is a classical computing system), provides quantum process 132/134 (second quantum service request, where the difference is that they are in a different language depending on the quantum platform), to each of simulated quantum computers 112 and 114)
receive, from each of the plurality of simulator processes, second simulation results of execution of the second quantum service request; and (THOMPSON, para. 0120: “The process then sends the instructions to the quantum computer (operation 918). In operation 918, the quantum computer can be an actual physical computer or a simulation. The process receives results from an execution of the instructions on the quantum computer (operation 920). The process saves the results (operation 922).”)
However, THOMPSON fails to explicitly teach:
determine, from the plurality of quantum computing devices, at least one optimal quantum computing device to optimize execution of the first quantum service request and the second quantum service request.
However, in a related field of endeavor (“an algorithm execution management system of a provider network” with respect to quantum computing resources, see para. 0014), KRNETA teaches:
determine, from the plurality of quantum computing devices, at least one optimal quantum computing device to optimize execution of the first quantum service request and the second quantum service request. (KRNETA, para. 0015: “based on the algorithm provided by the user, the algorithm execution management system may identify or select appropriate quantum computing resources (e.g., from a pool of quantum computing resources) for the user.”;
KRNETA, para. 0020: “By comparison, quantum computing resources 112 may include various quantum computers, quantum processing units (QPUs), and/or quantum hardware based on qubits. In some embodiments, quantum computing resources 112 may be implemented using qubits built from superconductors, trapped ions, semiconductors, photonics, etc.”;
KRNETA, para. 0042: “In some embodiments, quantum computing service 104 includes quantum hardware provider recommendation/selection module 420. As described above, in some embodiments, user 116 may specify quantum computing resources 112 to be used for executing the user’s algorithm. For example, user 116 may specify quantum computing resources 112 using an environment variable. Alternatively, in some embodiments, algorithm execution management system 106 may use quantum hardware recommendation/selection module 420 to make a recommendation to user 116 as to which type of quantum computer or which quantum hardware provider to use to execute a quantum program submitted by the user.”;
Examiner’s Note (EN): in combination with the benchmark comparison of 2 or more quantum computers as explained in THOMPSON, where benchmark parameters include processor execution time, memory used, accuracy, error, noise, circuit execution time, etc. (see paras. 0033, 0109), THOMPSON now benchmarks the different quantum computers and then selects the optimal quantum computing resource as in KRNETA; the examiner further notes that pursuant to MPEP 2144.04 VI, duplicating the same steps for the first and second quantum services request has “no patentable significance unless a new and unexpected result is produced.”)
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices. As disclosed by KRNETA, one of ordinary skill would have been motivated to do so because KRNETA provides an “algorithm execution management system [that] can provide better performance than executing algorithms from a user’s own environment. As described above, in some embodiments, the algorithm execution management system may provide the quantum tasks of the algorithm a priority during execution of the algorithm over the quantum tasks of other algorithms for using quantum computing resources. This warrants the quantum task of the algorithm to be executed ahead of the other quantum tasks, which can result in shorter and more predictable execution times for the algorithm.” (para. 0018). One of ordinary skill would further understand that the different quantum computing types (e.g., trapped ion vs. superconducting) have different physical limitations, and some types of quantum computing devices may work better for specific algorithms.
Regarding Claim 20
THOMPSON teaches:
A computer program product stored on a non-transitory computer-readable storage medium and including instructions to cause a processor device of a classical computing system to: (THOMPSON, para. 0131: “These instructions are referred to as program code, computer usable program code, or computer-readable program code that can be read and executed by a processor in processor unit 1004. The program code in the different embodiments can be embodied on different physical or computer-readable storage medium, such as memory 1006 or persistent storage 1008.”)
The remaining limitations in claim 20 correspond to the method of claim 1, and therefore claim 20 is rejected for the same reasons explained above with respect to claim 1 under 35 U.S.C. 103 in view of the THOMPSON, KRNETA, and RIGETTI references.
Claims 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over THOMPSON in view of KRNETA and RIGETTI and further in view of US 20200174836 A1, hereinafter referenced as GUNNELS.
Regarding Claim 9
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 1. However, THOMPSON, KRNETA, and RIGETTI fail to explicitly teach:
sending, by the classical computing system, an optimized first quantum service request to a scheduler, the optimized first quantum service request comprising the first quantum service request, and identification of the optimal qubit type.
However, in a related field of endeavor (quantum computer job scheduling, see para. 0001), GUNNELS teaches:
sending, by the classical computing system, an optimized first quantum service request to a scheduler, the optimized first quantum service request comprising the first quantum service request, and identification of the optimal qubit type. (GUNNELS, para. 0016: “the scheduler component can determine the run order based on at least one of: approximations of longest runtimes corresponding to the quantum computing jobs; availability of one or more preferred qubits; or a defined level of confidence corresponding to correctness of at least one of the quantum computing jobs. An advantage of such a system is that it can facilitate accurate solutions to computations executed by one or more quantum computing devices.”; (EN): in combination with THOMPSON and KRNETA and RIGETTI, the classical + quantum computer system of THOMPSON (as modified by KRNETA with respect to selecting an optimal quantum computing resource), now utilizes the scheduler of GUNNELS to optimize the service by selecting a quantum computer using the preferred qubit type).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices, and further with the scheduler of GUNNELS. As disclosed by GUNNELS, one of ordinary skill would have been motivated to do so because: “Quantum computing has the potential to solve problems that, due to their computational complexity, cannot be solved, either at all or for all practical purposes, on a classical computer. However, quantum computing requires very specialized skills to, for example, co-schedule quantum computing jobs based on quantum based run constraints, where such quantum computing jobs can be executed by a quantum computing device (e.g., a quantum computer, quantum processor, etc.) based on such a co-schedule. For example, based on such a co-schedule (e.g., also referred to as a run order throughout this disclosure), the quantum computing device can execute a certain quantum computing job using certain qubits.” (para. 0005).
Regarding Claim 11
THOMPSON, KRNETA, and RIGETTI disclose the method of claim 10. However, THOMPSON, KRNETA, and RIGETTI fail to explicitly teach:
sending, by the classical computing system, an optimized first quantum service request to a scheduler, the optimized first quantum service request comprising the first quantum service request, and identification of the optimal quantum computing device.
However, in a related field of endeavor (quantum computer job scheduling, see para. 0001), GUNNELS teaches:
sending, by the classical computing system, an optimized first quantum service request to a scheduler, the optimized first quantum service request comprising the first quantum service request, and identification of the optimal qubit type. (GUNNELS, para. 0016: “the scheduler component can determine the run order based on at least one of: approximations of longest runtimes corresponding to the quantum computing jobs; availability of one or more preferred qubits; or a defined level of confidence corresponding to correctness of at least one of the quantum computing jobs. An advantage of such a system is that it can facilitate accurate solutions to computations executed by one or more quantum computing devices.”; (EN): in combination with THOMPSON and KRNETA and RIGETTI, the classical + quantum computer system of THOMPSON (as modified by KRNETA with respect to selecting an optimal quantum computing resource), now utilizes the scheduler of GUNNELS to optimize the service by selecting a quantum computer using the preferred qubit type).
Before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the classical and quantum computer simulation teachings, as well as the benchmarking teachings, of THOMPSON, with the teachings of KRNETA with respect to the quantum hardware recommendation/selection module that selects specific quantum computing resources based on the quantum algorithm to be executed, and further with the teachings of RIGETTI concerning simulating different operating parameters for different qubit devices, and further with the scheduler of GUNNELS. As disclosed by GUNNELS, one of ordinary skill would have been motivated to do so because: “Quantum computing has the potential to solve problems that, due to their computational complexity, cannot be solved, either at all or for all practical purposes, on a classical computer. However, quantum computing requires very specialized skills to, for example, co-schedule quantum computing jobs based on quantum based run constraints, where such quantum computing jobs can be executed by a quantum computing device (e.g., a quantum computer, quantum processor, etc.) based on such a co-schedule. For example, based on such a co-schedule (e.g., also referred to as a run order throughout this disclosure), the quantum computing device can execute a certain quantum computing job using certain qubits.” (para. 0005).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL C. LEE/Examiner, Art Unit 2128
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