DETAILED ACTION
Applicant cancels claims 1-20; adds new claims 21-40.
Claims 21-40 rejected under 35 USC § 103.
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 .
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.
Claims 21-22, 25, 30-35, and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Dadashikelayeh et al., U.S. PG-Publication No. 2017/0357539 A1, in view of Rigetti et al., U.S. Patent No. 10,127,499 B1 (hereinafter Rigetti ‘499).
Claim 21
Dadashikelayeh discloses a system, comprising: one or more computing devices configured to implement a quantum computing service of a provider network. Dadashikelayeh discloses “methods that offer quantum-ready services and/or quantum-enabled services,” comprising “a quantum-enabled framework” that “may allow users to use both classical and quantum resources in a hybrid manner such that the framework intelligently chooses the right solver and the right parameters for each particular sub-problem or sub-task.” Dadashikelayeh, ¶ 30. The method facilitate quantum computing “in a distributed environment, such as over a network (e.g., in the cloud).” Id. at ¶¶ 32-33. The method is interfaced by a user via an “application programming interface (API).” Id. at ¶ 45.
Dadashikelayeh discloses wherein the one or more computing devices that implement the quantum computing service are configured to: receive, from a customer of the provider network, a quantum-classical machine learning algorithm to be executed using a classical computing resource and a quantum computing resource. Systems implementing the method include a “quantum computer … configured to perform one or more quantum algorithms” and a “classical computer … configured to perform one or more classical algorithms” operatively coupled to a digital computer. Id. at ¶ 37. A request 101 “is received by an application programming interface (API) gateway 111 and then forwarded to one or more target microservices” (request → quantum-classical machine learning algorithm to be executed). Id. at ¶ 48.
Dadashikelayeh discloses that the system “may provide a remote interface capable of solving computationally expensive problems by deciding if a problem may be solved efficiently on a quantum-ready or a classical computing service,” wherein the service is “able to … decompose or break down the problem and delegate appropriate components of the computational task to a quantum-ready or a classical service.” Id. at ¶ 65. The system provides “a cloud-based framework to provide hybrid quantum-enabled computing problems … using a classical computer for some portion of the work and a quantum … computer … for the remaining portion of the work.” Id. at ¶ 66. The system comprises “a series of sub-processes that may involve intelligently decomposing a hard … computational task into simpler … sub-problems,” then decides “how to distribute the decomposed tasks between a plurality of classical computation resources and quantum-ready computation services.” Id. at ¶ 68.
Dadashikelayeh discloses translate a portion of the quantum-classical machine learning algorithm into a low-level language that can be run on the quantum computing resource. A cluster manager divides a computational task “into two or more computational components,” wherein the components “are translated into one or more quantum algorithms, or translated into quantum machine instructions.” Translations into quantum machine instructions “comprises determination of a number of qubits and/or determination of a quantum operator.” Id. at ¶¶ 54-56. A worker may determine “if the classical algorithm or the quantum algorithm has to be translated into another classical algorithm or another quantum algorithm.” Id. at ¶ 59.
Dadashikelayeh discloses submit the translated portion and [another] translated … portion of the quantum-classical machine learning algorithm for execution using the classical computing resource and the quantum computing resource; and receive results of the submitted execution. The system comprises “a cluster manager … configured to create an instance/container (also ‘worker’ herein) to (1) translate the request in [a] queue into one or more quantum machine instructions, (2) deliver the one or more quantum machine instructions to the quantum computer over the network to perform the computational task, and (3) receive one or more solutions from the quantum computer.” Id. at ¶ 44. In one embodiment, “one or more solutions from each of the quantum ready service 603 and the classical service 604 may be directed to [an] arbiter 602,” and “an indication of a solution to the computational task may be provided to the client or the user,” wherein the “indication may include the solution or the individual solutions.” Id. at ¶ 67; FIG. 6.
Dadashikelayeh does not expressly disclose translate, by a library, another portion of the quantum-classical machine learning algorithm into control signals that natively control qubits of the quantum computing resource.
Rigetti ‘499 discloses translate, by a library, another portion of the quantum-classical machine learning algorithm into control signals that natively control qubits of the quantum computing resource. Rigetti ‘499 discloses “a heterogeneous computing system that combines a quantum processor with one or more other computing hardware devices, which may include classical computing devices,” wherein different segments of a problem are “sent to different hardware types for processing.” Rigetti ‘499, 2:8-20. The system comprises “a quantum computer control system” that “includes a classical processor (e.g., a GPU or multiprocessor accelerator) that performs classical information processing tasks,” that is “used to improve or optimize operation of the quantum processor.” Id. at 2:49-60. Control signals are used to “manipulate the quantum states of individual qubits and the couplings between qubits” (manipulate states of individual qubits → natively control qubits). Id. at 3:25-29.
A program interface “provides control information” and includes “a classical computing cluster, servers, databases, networks, or other types of classical computing equipment.” The program interface “can generate control information, for example, based on a quantum task or a quantum algorithm to be performed by the quantum processor unit 100.” (generate control information from task → translate a portion into control signals). Id. at 5:39-52; See Also 6:10-12 (“control signals delivered on each clock cycle can be configured, for example, based on the sequence of instructions”). A host unit “identifies the data processing tasks to be performed by each type of processor device,” wherein “the host unit may have access to a library of functions that each processor device can perform.” The host unit can then “delegate the computing tasks … according to the computer program or another protocol.” Id. at 16:48-57.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid quantum-classical processing system of Dadashikelayeh to incorporate using a classical computer to send control signals for setting qubits in a quantum computer as taught by Rigetti ‘499. One of ordinary skill in the art would be motivated to integrate a classical computer to send control signals for setting qubits in a quantum computer into Dadashikelayeh, with a reasonable expectation of success, in order to “offload computationally-intensive tasks to the specialized processors, for example, to improve system performance, system utilization, or other factors.” Rigetti ‘499 at 13:43-50.
Claim 22
Dadashikelayeh discloses wherein the quantum-classical machine learning algorithm comprises a translation of a machine learning algorithm into quantum circuits to be executed using the classical computing resource and the quantum computing resource. A cluster manager divides a computational task “into two or more computational components,” wherein the components “are translated into one or more quantum algorithms, or translated into quantum machine instructions.” Translations into quantum machine instructions “comprises determination of a number of qubits and/or determination of a quantum operator.” Dadashikelayeh, ¶¶ 54-56. A worker may determine “if the classical algorithm or the quantum algorithm has to be translated into another classical algorithm or another quantum algorithm.” Id. at ¶ 59.
Claim 25
Dadashikelayeh discloses wherein the one or more computing devices that implement the quantum computing service are further configured to: generate an aggregated result of the received results; and return the aggregated result to the customer. A cluster manager divides a computational task “into two or more computational components,” wherein the components “are translated into one or more quantum algorithms, or translated into quantum machine instructions.” Translations into quantum machine instructions “comprises determination of a number of qubits and/or determination of a quantum operator.” The cluster manager is “configured to aggregate solutions of the two or more computational components.” Dadashikelayeh, ¶¶ 54-57.
Claim 30
Dadashikelayeh discloses further comprising the quantum computing resource, wherein the quantum computing resource is within the provider network. The method provides “quantum computing in a distributed environment, such as over a network (e.g., in the cloud), wherein “a user at a first location may submit a request for a calculation or task to pe performed by a quantum computer … at a second location that is remotely located with respect to the first location.” Dadashikelayeh, ¶ 32.
Claim 31
Dadashikelayeh discloses wherein the quantum computing resource is managed by a different entity than the provider network. The system comprises “a transactional unit for receiving an item in value of exchange for at least executing the one or more instructions to generate the one or more solutions,” wherein the unit “may determine a cost for executing the one or more instructions.” The instructions “may be executed upon receiving authorization to execute one or more instructions.” Dadashikelayeh, ¶ 74.
Claim 32
Dadashikelayeh discloses wherein: the one or more computing devices, configured to implement the quantum computing service, are further configured to implement the library; and the one or more computing devices configured to implement the library are further configured to: responsive to the reception, from the customer, of the quantum-classical machine learning algorithm, determine that the quantum-classical machine learning algorithm is to be executed using the classical computing resource and the quantum computing resource, in addition to one or more classical accelerators. Dadashikelayeh discloses that the system “may provide a remote interface capable of solving computationally expensive problems by deciding if a problem may be solved efficiently on a quantum-ready or a classical computing service,” wherein the service is “able to … decompose or break down the problem and delegate appropriate components of the computational task to a quantum-ready or a classical service.” Dadashikelayeh, ¶ 65. The system provides “a cloud-based framework to provide hybrid quantum-enabled computing problems … using a classical computer for some portion of the work and a quantum … computer … for the remaining portion of the work.” Id. at ¶ 66. The system comprises “a series of sub-processes that may involve intelligently decomposing a hard … computational task into simpler … sub-problems,” then decides “how to distribute the decomposed tasks between a plurality of classical computation resources and quantum-ready computation services.” Id. at ¶ 68.
Claim 33
Rigetti ‘499 discloses wherein the one or more computing devices configured to implement the library are further configured to: responsive to the translation of the other portion into control signals that natively control the qubits of the quantum computing resource, provide the control signals to the one or more classical accelerators. Rigetti ‘499 discloses “a heterogeneous computing system that combines a quantum processor with one or more other computing hardware devices, which may include classical computing devices,” wherein different segments of a problem are “sent to different hardware types for processing.” Rigetti ‘499, 2:8-20. The system comprises “a quantum computer control system” that “includes a classical processor (e.g., a GPU or multiprocessor accelerator) that performs classical information processing tasks,” that is “used to improve or optimize operation of the quantum processor.” Id. at 2:49-60. Control signals are used to “manipulate the quantum states of individual qubits and the couplings between qubits” (manipulate states of individual qubits → natively control qubits). Id. at 3:25-29.
A program interface “provides control information” and includes “a classical computing cluster, servers, databases, networks, or other types of classical computing equipment.” The program interface “can generate control information, for example, based on a quantum task or a quantum algorithm to be performed by the quantum processor unit 100.” (generate control information from task → translate a portion into control signals). Id. at 5:39-52; See Also 6:10-12 (“control signals delivered on each clock cycle can be configured, for example, based on the sequence of instructions”). A host unit “identifies the data processing tasks to be performed by each type of processor device,” wherein “the host unit may have access to a library of functions that each processor device can perform.” The host unit can then “delegate the computing tasks … according to the computer program or another protocol.” Id. at 16:48-57.
Claim 34
Rigetti ‘499 discloses wherein the one or more classical accelerators comprise one or more of: a graphics processing unit (GPU); a field programmable gate array (FPGA); or an application-specific integrated circuit (ASIC). Rigetti ‘499 discloses “a heterogeneous computing system that combines a quantum processor with one or more other computing hardware devices, which may include classical computing devices,” wherein different segments of a problem are “sent to different hardware types for processing.” Rigetti ‘499, 2:8-20. The system comprises “a quantum computer control system” that “includes a classical processor (e.g., a GPU or multiprocessor accelerator) that performs classical information processing tasks,” that is “used to improve or optimize operation of the quantum processor.” Id. at 2:49-60. Control signals are used to “manipulate the quantum states of individual qubits and the couplings between qubits” (manipulate states of individual qubits → natively control qubits). Id. at 3:25-29.
Claim 35
Claim 35 is rejected utilizing the aforementioned rationale for Claim 21; the claim is directed to a method performed by the system.
Claim 39
Claim 39 is rejected utilizing the aforementioned rationale for Claim 21; the claim is directed to a medium storing instructions executed by the system.
Claims 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Dadashikelayeh et al., U.S. PG-Publication No. 2017/0357539 A1, in view of Rigetti et al., U.S. Patent No. 10,127,499 B1 (hereinafter Rigetti ‘499), further in view of Rigetti et al., U.S. PG-Publication No. 2016/0267032 A1 (hereinafter Rigetti ‘032).
Claim 23
Dadashikelayeh discloses wherein the one or more computing devices that implement the quantum computing service are further configured to: prepare instructions to be used to execute the quantum-classical machine learning algorithm, wherein the instructions comprise indications to: … run the quantum-classical machine learning algorithm based, at least in part, on the translated portion and the translated other portion; and measure results of the run of the quantum-classical machine learning algorithm; and submit the prepared instructions, along with the translated portion and the translated other portion of the quantum-classical machine learning algorithm, for execution using the classical computing resource and the quantum computing resource. The system comprises “a cluster manager … configured to create an instance/container (also ‘worker’ herein) to (1) translate the request in [a] queue into one or more quantum machine instructions, (2) deliver the one or more quantum machine instructions to the quantum computer over the network to perform the computational task, and (3) receive one or more solutions from the quantum computer.” Id. at ¶ 44. In one embodiment, “one or more solutions from each of the quantum ready service 603 and the classical service 604 may be directed to [an] arbiter 602,” and “an indication of a solution to the computational task may be provided to the client or the user,” wherein the “indication may include the solution or the individual solutions.” Id. at ¶ 67; FIG. 6.
Rigetti ‘499 discloses that control signals “can manipulate the quantum states of individual qubits.” Rigetti, ‘499 at 3:25-26.
Dadashikelayeh-Rigetti ‘499 does not expressly disclose wherein the instructions initialize the quantum computing resource.
Rigetti ‘032 discloses wherein the instructions initialize the quantum computing resource. Rigetti ‘032 discloses a control system 110 comprising “one or more classical computers” that “controls operations of [a] quantum processor cell 102.” Rigetti ‘032, ¶ 46. The control signals “can manipulate the quantum states of individual qubits and the couplings between qubits.” Id. at ¶ 38. The quantum processor cell 102 comprises qubits that “each store a single bit of quantum information, and the qubits can collectively define the computational state of a quantum processor.” The quantum processor cell 102 “can process the quantum information stored in the qubits by applying control signals to the qubits … housed in the quantum processor cell.” Id. at ¶¶ 42-43. In one embodiment, the system includes qubit devices “where control signals are used for … encoding an initial state” (encoding initial state → initialize quantum resource with qubit initial values). Id. at ¶ 378.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid quantum-classical processing system of Dadashikelayeh-Rigetti ‘499 to incorporate using initializing the quantum computing resources using control signals of Rigetti ‘032. One of ordinary skill in the art would be motivated to integrate a initializing the quantum computing resources using control signals into Dadashikelayeh-Rigetti ‘499, with a reasonable expectation of success, in order to tune qubits for “improving performance of the qubit devices.” Rigetti ‘032, ¶ 83.
Claim 24
Rigetti ‘032 discloses wherein to initialize the quantum computing resource, the one or more computing devices are further configured to prepare instructions that cause qubits of the quantum computing resource to be set to initial values. Rigetti ‘032 discloses a control system 110 comprising “one or more classical computers” that “controls operations of [a] quantum processor cell 102.” Rigetti ‘032, ¶ 46. The control signals “can manipulate the quantum states of individual qubits and the couplings between qubits.” Id. at ¶ 38. The quantum processor cell 102 comprises qubits that “each store a single bit of quantum information, and the qubits can collectively define the computational state of a quantum processor.” The quantum processor cell 102 “can process the quantum information stored in the qubits by applying control signals to the qubits … housed in the quantum processor cell.” Id. at ¶¶ 42-43. In one embodiment, the system includes qubit devices “where control signals are used for … encoding an initial state” (encoding initial state → initialize quantum resource with qubit initial values). Id. at ¶ 378.
Claims 26, 28-29, 36, 38, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Dadashikelayeh et al., U.S. PG-Publication No. 2017/0357539 A1, in view of Rigetti et al., U.S. Patent No. 10,127,499 B1 (hereinafter Rigetti ‘499), further in view of Lanting et al., U.S. PG-Publication No. 2018/030264 A1.
Claim 26
Lanting discloses wherein the quantum-classical machine learning algorithm, received from the customer, comprises an indication of a number of times to repeat an execution of the quantum-classical machine learning algorithm. Lanting discloses a “method for mitigating degeneracy in a hybrid computing system that includes a quantum processor and a digital processor, “ wherein the method operates as “a sample generator providing samples,” comprising the steps of “sending a problem to the quantum processor; iteratively repeating until an exit criterion is met; drawing a plurality of samples by the quantum processor; returning the plurality of samples to the digital processor” in order to calculate “a normalized floppiness metric.” The exit criterion is an “exit condition” that includes “completing a determined number of iterations” (i.e., an indication of a number of times to repeat an execution). Lanting, ¶¶ 24-25. Figures 1A-B illustrates method 100 performed “in response to submission of a problem.” At 110, “the hybrid computer sends a problem to … a quantum processor that is communicatively coupled to a digital computer.” At 115, the hybrid computer “collects a number of samples,” and at 130 “determines if the qubit or domain of qubits is floppy, i.e., if the state of the qubit … can be flipped without changing the energy. The process “may repeat while there are additional qubits or domains of qubits to check for floppiness.” At 170, “the hybrid computer determines whether an exit criterion has been met,” wherein the criteria includes “thresholds cased on the computation time and the number of iterations.” Id. at ¶¶ 110-119.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid quantum-classical processing system of Dadashikelayeh-Rigetti ‘499 to incorporate setting a number of iterations to repeat processing of Lanting. One of ordinary skill in the art would be motivated to integrate a setting a number of iterations to repeat processing into Dadashikelayeh-Rigetti ‘499, with a reasonable expectation of success, in order to “mitigate degeneracy by tuning tunneling rates” in order to “significantly boost hardware performance on degeneracy-prone problems, and/or may improve hardware performance for more general problem sets.” Lanting, ¶ 100.
Claim 28
Lanting discloses wherein the one or more computing devices that implement the quantum computing service are further configured to: determine that the quantum-classical machine learning algorithm is a job to be executed by the quantum computing service based, at least in part, on the reception of the indication of the number of times to repeat the execution of the quantum-classical machine learning algorithm. Lanting discloses a “method for mitigating degeneracy in a hybrid computing system that includes a quantum processor and a digital processor, “ wherein the method operates as “a sample generator providing samples,” comprising the steps of “sending a problem to the quantum processor; iteratively repeating until an exit criterion is met; drawing a plurality of samples by the quantum processor; returning the plurality of samples to the digital processor” in order to calculate “a normalized floppiness metric.” The exit criterion is an “exit condition” that includes “completing a determined number of iterations” (i.e., an indication of a number of times to repeat an execution). Lanting, ¶¶ 24-25. Figures 1A-B illustrates method 100 performed “in response to submission of a problem.” At 110, “the hybrid computer sends a problem to … a quantum processor that is communicatively coupled to a digital computer.” At 115, the hybrid computer “collects a number of samples,” and at 130 “determines if the qubit or domain of qubits is floppy, i.e., if the state of the qubit … can be flipped without changing the energy. The process “may repeat while there are additional qubits or domains of qubits to check for floppiness.” At 170, “the hybrid computer determines whether an exit criterion has been met,” wherein the criteria includes “thresholds cased on the computation time and the number of iterations.” Id. at ¶¶ 110-119.
Claim 29
Dadashikelayeh discloses wherein the one or more computing devices that implement the quantum computing service are further configured to: generate an aggregated result of the job, based, at least in part, on the received results; and return the aggregated result of the job to the customer. A cluster manager divides a computational task “into two or more computational components,” wherein the components “are translated into one or more quantum algorithms, or translated into quantum machine instructions.” Translations into quantum machine instructions “comprises determination of a number of qubits and/or determination of a quantum operator.” The cluster manager is “configured to aggregate solutions of the two or more computational components.” Dadashikelayeh, ¶¶ 54-57.
Claims 36 and 38
Claims 36 and 38 are rejected utilizing the aforementioned rationale for Claims 26-28; the claims are directed to a method performed by the system.
Claim 40
Lanting discloses receive an indication of a number of times to repeat the execution of the quantum-classical machine learning algorithm; and determine that the quantum-classical machine learning algorithm is a job to be executed using the classical computing resource, the quantum computing resource, and the one or more classical accelerators based, at least in part, on the indication of the number of times to repeat the execution. Lanting discloses a “method for mitigating degeneracy in a hybrid computing system that includes a quantum processor and a digital processor, “ wherein the method operates as “a sample generator providing samples,” comprising the steps of “sending a problem to the quantum processor; iteratively repeating until an exit criterion is met; drawing a plurality of samples by the quantum processor; returning the plurality of samples to the digital processor” in order to calculate “a normalized floppiness metric.” The exit criterion is an “exit condition” that includes “completing a determined number of iterations” (i.e., an indication of a number of times to repeat an execution). Lanting, ¶¶ 24-25. Figures 1A-B illustrates method 100 performed “in response to submission of a problem.” At 110, “the hybrid computer sends a problem to … a quantum processor that is communicatively coupled to a digital computer.” At 115, the hybrid computer “collects a number of samples,” and at 130 “determines if the qubit or domain of qubits is floppy, i.e., if the state of the qubit … can be flipped without changing the energy. The process “may repeat while there are additional qubits or domains of qubits to check for floppiness.” At 170, “the hybrid computer determines whether an exit criterion has been met,” wherein the criteria includes “thresholds cased on the computation time and the number of iterations.” Id. at ¶¶ 110-119.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid quantum-classical processing system of Dadashikelayeh-Rigetti ‘499 to incorporate setting a number of iterations to repeat processing of Lanting. One of ordinary skill in the art would be motivated to integrate a setting a number of iterations to repeat processing into Dadashikelayeh-Rigetti ‘499, with a reasonable expectation of success, in order to “mitigate degeneracy by tuning tunneling rates” in order to “significantly boost hardware performance on degeneracy-prone problems, and/or may improve hardware performance for more general problem sets.” Lanting, ¶ 100.
Dadashikelayeh discloses cause the classical computing resource, the quantum computing resource, and the one or more classical accelerators to begiven permission to interact with one another for the duration of the execution of the quantum-classical machine learning algorithm, and for the number of times the execution is to be repeated. The system comprises “a transactional unit for receiving an item in value of exchange for at least executing the one or more instructions to generate the one or more solutions,” wherein the unit “may determine a cost for executing the one or more instructions.” The instructions “may be executed upon receiving authorization to execute one or more instructions.” Dadashikelayeh, ¶ 74.
Claims 27 and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Dadashikelayeh et al., U.S. PG-Publication No. 2017/0357539 A1, in view of Rigetti et al., U.S. Patent No. 10,127,499 B1 (hereinafter Rigetti ‘499), further in view of Lanting et al., U.S. PG-Publication No. 2018/030264 A1, further in view of Rigetti et al., U.S. PG-Publication No. 2016/0267032 A1 (hereinafter Rigetti ‘032).
Claim 27
Lanting discloses wherein the one or more computing devices that implement the quantum computing service are further configured to: prepare instructions to be used to execute the quantum-classical machine learning algorithm, wherein the instructions comprise indications to … run the quantum-classical machine learning algorithm based, at least in part, on the translated portion and the translated other portion; measure results of the run of the quantum-classical machine learning algorithm; and repeat said initialize, said run, and said measure based, at least in part, on the indication of the number of times to repeat the execution of the quantum-classical machine learning algorithm; and submit the prepared instructions, along with the translated portion and the translated other portion of the quantum-classical machine learning algorithm, for execution using the classical computing resource and the quantum computing resource. Lanting discloses a “method for mitigating degeneracy in a hybrid computing system that includes a quantum processor and a digital processor, “ wherein the method operates as “a sample generator providing samples,” comprising the steps of “sending a problem to the quantum processor; iteratively repeating until an exit criterion is met; drawing a plurality of samples by the quantum processor; returning the plurality of samples to the digital processor” in order to calculate “a normalized floppiness metric.” The exit criterion is an “exit condition” that includes “completing a determined number of iterations” (i.e., an indication of a number of times to repeat an execution). Lanting, ¶¶ 24-25. Figures 1A-B illustrates method 100 performed “in response to submission of a problem.” At 110, “the hybrid computer sends a problem to … a quantum processor that is communicatively coupled to a digital computer.” At 115, the hybrid computer “collects a number of samples,” and at 130 “determines if the qubit or domain of qubits is floppy, i.e., if the state of the qubit … can be flipped without changing the energy. The process “may repeat while there are additional qubits or domains of qubits to check for floppiness.” At 170, “the hybrid computer determines whether an exit criterion has been met,” wherein the criteria includes “thresholds cased on the computation time and the number of iterations.” Id. at ¶¶ 110-119.
Dadashikelayeh-Rigetti ‘499-Lanting does not expressly disclose wherein the instructions initialize the quantum computing resource.
Rigetti ‘032 discloses wherein the instructions initialize the quantum computing resource. Rigetti ‘032 discloses a control system 110 comprising “one or more classical computers” that “controls operations of [a] quantum processor cell 102.” Rigetti ‘032, ¶ 46. The control signals “can manipulate the quantum states of individual qubits and the couplings between qubits.” Id. at ¶ 38. The quantum processor cell 102 comprises qubits that “each store a single bit of quantum information, and the qubits can collectively define the computational state of a quantum processor.” The quantum processor cell 102 “can process the quantum information stored in the qubits by applying control signals to the qubits … housed in the quantum processor cell.” Id. at ¶¶ 42-43. In one embodiment, the system includes qubit devices “where control signals are used for … encoding an initial state” (encoding initial state → initialize quantum resource with qubit initial values). Id. at ¶ 378.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the hybrid quantum-classical processing system of Dadashikelayeh-Rigetti ‘499-Lanting to incorporate using initializing the quantum computing resources using control signals of Rigetti ‘032. One of ordinary skill in the art would be motivated to integrate a initializing the quantum computing resources using control signals into Dadashikelayeh-Rigetti ‘499-Lanting, with a reasonable expectation of success, in order to tune qubits for “improving performance of the qubit devices.” Rigetti ‘032, ¶ 83.
Claim 37
Claim 37 is rejected utilizing the aforementioned rationale for Claim 27; the claim is directed to a method performed by the system.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Dukatz et al., U.S. PG-Publication No. 2018/0308000 A1.
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/FRANK D MILLS/Primary Examiner, Art Unit 2194 January 9, 2026