DETAILED ACTION
Status of Claims
Claim(s) 1, 3-11, and 13-20 are pending and are examined herein.
Claim(s) 1, 8-9, 11, and 18-19 have been Amended. Claim(s) 2 and 12 are Canceled.
Claim(s) 1, 3-11, and 13-20 remain rejected under 35 U.S.C. § 101 and 35 U.S.C. § 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 .
Response to Amendment
The amendment filed on June 24, 2026, has been entered. Claims 1, 3-11, and 13-20 are pending in the application. Applicant’s amendments to the claims have overcome the nonstatutory double patenting rejection previously set forth in the Non-Final Office Action mailed on July 01, 2025. Applicant’s amendments to the claims have been fully considered and are addressed in the rejections below.
Response to Arguments
Applicant's arguments with respect to the rejection under 35 U.S.C. § 101, filed on 06/24/2026, have been fully considered but they are not persuasive. (See Remarks pp. 7-8).
Applicant argues that the claimed features “are related to a specific quantum circuit optimization process rather than generic resource scheduling. Further, these features cannot be practically performed in human mind with pen and paper.”
The examiner respectfully disagrees. While the claim features may be used for quantum circuit optimization process, this merely defines the technological environment or field of use of the claim process. The claim is primarily directed to the process of predicting runtime characteristics concerning a quantum computing function, predicting cuttability of a quantum circuit based on hardware constraints, predicting the number of sub-circuits, selecting an execution environment, and generating an execution plan for the sub-circuits. These steps represent evaluation and decision-making processes that can be practically performed in the human mind and/or with physical aid (e.g., pen and paper). Accordingly, these steps fall under the abstract idea of mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Applicant further argues that “Even assuming, arguendo, that independent claims 1 and 11 recite a judicial exception, the above-recited features, which include predictions of cuttability and the number of subcircuits, selection of an execution environment, generation of the execution plan, and execution of the quantum computing function, improve the functioning of a quantum computer. Thus, independent claims 1 and 11 are believed to integrate the alleged abstract idea into a practical application.”
The examiner respectfully disagrees. Under Step 2A, Prong 2, the claimed additional elements are not sufficient to integrate the abstract idea into a practical application. It is noted that the recited features, including predictions of cuttability and the number of sub-circuits, selection of an execution environment, and generation of the execution plan are steps that fall under the mental process grouping. As explained above, these steps represent observation, evaluation, and decision-making processes. The claimed additional feature of “executing the quantum computing function in the execution environment according to the execution plan” amounts to no more than merely the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The use of a computer or other machinery in its ordinary capacity merely as a tool to execute a generic computer function. See MPEP § 2106.05(f).
Furthermore, it is important to note that the judicial exception alone cannot provide improvement; rather, the improvement can be provided by one or more additional elements beyond the judicial exception itself. Specifically, the "improvements" analysis in Step 2A determines whether the claim pertains to an improvement to the functioning of a computer or to another technology without reference to what is well-understood, routine, conventional activity. See § 2106.04(d).
Accordingly, the additional elements, whether considered individually or as an ordered combination with the abstract idea, represent the use a computer or other machinery in its ordinary capacity merely as a tool to execute a generic computer function and/or generally linking the use of the judicial exception to a particular technological environment or field of use, which are not sufficient to transform the abstract idea into a patent-eligible application nor provides significantly more than the abstract idea itself.
In view of the above, the rejection under 35 U.S.C. § 101 is maintained. For more details on the subject matter eligibility analysis of the claim, the Examiner respectfully refers to the rejection under 35 U.S.C. § 101.
Applicant's arguments, with respect to the rejection under 35 U.S.C. § 103, filed on 06/24/2026, have been fully considered but are not persuasive and are moot in view of the new grounds of rejection necessitated by amendments. (See Remarks pp. 9-10)
Specifically, Applicant argues that the cited references do not disclose "predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources; in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit," as recited in independent claims 1 and 11."
However, Applicant’s arguments are moot in view of the new grounds of rejection necessitated by amendments. The examiner notes that at least independent claims 1 and 11 are currently rejected as being unpatentable over the combination of Durazzo, Davis, Tang, and Basu.
The examiner refers to the updated rejection under 35 U.S.C. § 103 for more details.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 1 and 3-10 recite a method (representing a process); and
Claims 11 and 13-20 recite a non-transitory storage medium (representing an article of manufacture);
Therefore, each set of claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Claim(s)
1, 3-11, and 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, and hence is not patent-eligible subject matter.
Regarding Currently amended Claim 1,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
predicting, using a machine learning model, runtime characteristics concerning a quantum computing function, which comprises a circuit-cutting process for a quantum circuit; (An abstract idea of a mental process. Examiner’s note: the “predicting” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). The broader recitation of predicting runtime characteristics concerning a quantum computing function falls under the mental process category. For example, an individual (e.g., a data scientist) can mentally predict the execution time required to perform a quantum computing function. This is a decision-making process that can be performed in the human mind.)
predicting, using the machine learning model, resources needed to perform the quantum computing function; (An abstract idea of a mental process. Examiner’s note: the “predicting” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). The broader recitation of predicting resources needed to perform the quantum computing function falls under the mental process category of abstract idea. For example, a person looks at a quantum algorithm, counts how many qubits it needs, estimates how long it will take to run, and decides if the available hardware component can handle it. This is an evaluation and judgment process that could be performed mentally without a computer.)
predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources; (An abstract idea of a mental process. Examiner’s note: the “predicting” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). This step defines a conditional decision-making process, which is an act of evaluation and judgment that can be practically performed in the human mind. For example, an individual can manually determine whether a given logical configuration of quantum circuit can be cut to satisfy given hardware constraints. This is an evaluation and judgment process that could be performed mentally without a computer.)
in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit; (An abstract idea of a mental process. Examiner’s note: the “predicting” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. But for the recitation of a machine learning model, that is not other than using a computer to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). This step is part of the conditional evaluation step, which involves determining the number of partitions. This step involves observation and decision-making process that fall under the mental process grouping.)
selecting an execution environment for the quantum computing function based on predicted cuttability and the predicted number of sub-circuits; (An abstract idea of a mental process. Examiner’s note: the “selecting” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. This involves an evaluation and decision-making process that can be performed in the human mind. For example, a person considers the determined runtime and resources requirements of a quantum function and decides whether to run it on a particular device or cloud computing environment. This is a mental process. See MPEP § 2106.04(a)(2)(I) & (III).)
generating an execution plan for the plurality of sub-circuits based on the predicted number of sub-circuits and hardware constraints of the available quantum computing resources; (An abstract idea of a mental process. Examiner’s note: the “generating” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind with the aid of pen and paper. This involves observation, evaluation, and decision-making processes that can be performed in the human mind. For example, an individual can manually generate a schedule for the sub-circuits based on hardware constraints and configuration analysis. This is a mental process. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 2A Prong 2: Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application.
Additional Elements Analysis:
The claim recite the additional element such as:
“using a machine learning model” (This amounts to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). In other words, the claim invokes computer and/or other machinery in its ordinary capacity merely as a tool to perform the abstract idea.)
“executing the quantum computing function in the execution environment according to the execution plan.” (This amounts to either including instructions to implement an abstract idea on a computer or using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f) and/or generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h). This step ties the abstract idea of predictions, selection, and plan generation to a particular technological environment (e.g., quantum computing environment). However, merely linking the judicial exception to a field of use does not by itself integrate the abstract idea into a practical application.)
Step 2B: Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept).
Additional Elements Analysis:
As explained above, the claimed additional elements merely represent generic computer components (i.e., conventional models) configured to perform the abstract ideas and generally link the abstract ideas into a technological environment. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception.
Therefore, claim 1 does not recite patent-eligible subject matter.
Regarding Original Claim 3,
Step 2A Prong 1: Claim 3, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the quantum computing function comprises a quantum circuit execution. (That is part of the abstract idea recited in claim 1. This limitation merely specifies the type of quantum function for which the prediction of runtime and resources requirements and selection are performed and therefore falls within the same abstract idea recited in claim 1. The claim does not introduce any technical implementation of the abstract idea that would be considered as additional element and evaluated under Step 2A, Prong 2.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 3 is ineligible.
Regarding Original Claim 4,
Step 2A Prong 1: Claim 4, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself. (The claim introduces two additional elements including:
collecting telemetry data (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This represents a generic computer function (i.e., data gathering in conjunction with the abstract idea).); and
using the telemetry data to retrain the machine learning model (This amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). This represents high-level training a model using the obtained data, which amounts to no more than invoking computer or other machinery in their ordinary capacity as a tool to perform an existing process.)
The additional elements do not integrate the abstract idea into a practical application; they merely perform routine data gathering and generic model training.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Collecting telemetry data represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. Further, the high-level recitation of model retraining amounts to generic and conventional computer component and does not recite an inventive concept. See MPEP § 2106.05(d).
Therefore, claim 4 is ineligible.
Regarding Original Claim 5,
Step 2A Prong 1: Claim 5, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein metadata and runtime characteristics relating to the quantum computing function are collected from the execution environment while the quantum computing function is being executed. (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This describes obtaining information from a system during execution (i.e., collecting metadata and runtime characteristics). This does not integrate the abstract idea into a practical application; it is a routine operation for obtaining data. This additional element does not transform the abstract idea into a practical application and is considered generic data gathering operation.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Collecting metadata of execution represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 5 is ineligible.
Regarding Original Claim 6,
Step 2A Prong 1: Claim 6, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the predicting of the runtime characteristics and/or the predicting of the resources, by the machine learning model, are performed based on inputs comprising any one or more of hybrid algorithm, service level objective, simulation engine, and available hardware. (That is part of the abstract idea recited in claim 1. The claim only specifies inputs to the model. The claim merely restates the concept of performing predictions of runtime/resources, which is a mental or algorithmic process that can be mentally performed. Merely specifying the input used by the model does not provide any technical improvement that would transform the abstract idea into a practical application.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
The claim does not introduce new additional elements beyond the machine learning model already recited in claim 1. The machine learning model amounts to merely invoking generic computer components as a tool to perform the abstract idea. The inputs (hybrid algorithm, SLO, simulation engine, hardware) merely define what information is used and do not constitute an inventive or unconventional technical element. Accordingly, claim 6 does not provide additional elements sufficient to integrate the abstract idea into a practical application.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. The merely define the type of input used to make the prediction and the use of the machine learning model does amount to inventive concept as it merely invokes generic computer component or other machinery in their ordinary capacity as a tool to perform the abstract idea.
Therefore, claim 6 is ineligible.
Regarding Original Claim 7,
Step 2A Prong 1: Claim 7, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the resources are used to retrain the machine learning model when demand for those resources permits. (The newly introduced limitation in claim 7 recites an abstract idea because it is directed to evaluation and conditional model update. The limitation involves deciding when to retain the machine learning model based on resources availability. The claim does not specify the technical aspect of this conditional determination to retrain the model when demands for those resources permits. This high-level recitation represents a decision-making process about retraining based on available resources, but does not define how it is technically accomplished.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
As noted above, claim 7 does not introduce new additional elements beyond what was already recited in claim 1 (machine learning model, computing environment). No hardware configuration or technological implementation is specified. This claim does not recite additional elements sufficient to integrate the abstract idea into a practical application.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, claim 7 is ineligible.
Regarding Currently amended Claim 8,
Step 2A Prong 1: Claim 8, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the runtime characteristics concerning the circuit cutting process comprise a prediction as to how many sub-circuits can be created from a circuit that is a subject of the circuit cutting process. (This limitation forms part of the abstract idea of claim 1. The claim merely specifies that the predicted runtime characteristics relate to a circuit cutting process and include estimating the number of sub-circuits that would result from partitioning a quantum circuit. This step is directed to evaluating or estimating circuit partitioning, which is a mental process and/or mathematical process. For example, a person could conceptually examine a quantum circuit diagram, determine where the circuit could be partitioned, and estimate the number of resulting sub-circuits. Furthermore, the claim does not specify any particular technical implementation of the circuit cutting process, such as whether the circuit cutting process represents a specific physical configuration or a logical decomposition. Instead, the limitation broadly recites predicting the outcome of such partitioning. Accordingly, the limitation represents an evaluation and estimation step that would fall within the abstract idea processes.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 8 is ineligible.
Regarding Currently amended Claim 9,
Step 2A Prong 1: Claim 9, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the predicting of the runtime characteristics comprises predicting that the circuit cutting process can be performed. (This limitation recites predicting whether a circuit cutting process can be performed, which is an evaluation or possibility determination regarding a computational operation. This represents decision-making process, which can be conceptually performed by examining the circuit diagram and determining whether it can be partitioned. Thus, this limitation is directed to evaluation and judgement regarding circuit cutting process, which falls within the mental process grouping of abstract idea. See MPEP § 2106.04(a)(2)(III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 9 is ineligible.
Regarding Original Claim 10,
Step 2A Prong 1: Claim 10, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
wherein the machine learning model is trained in real-time as telemetry is received concerning execution of another quantum computing function. (The telemetry aspect amounts to data gathering associated with the abstract idea of predicting runtime and resources. This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). The real-time training of the machine learning model to perform the abstract idea amounts to merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception. Furthermore, collecting telemetry data represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 10 is ineligible.
Regarding Currently amended Claim 11,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 11. The only difference is that claim 1 is drawn to a method, and claim 11 is drawn to a non-transitory storage medium. The recitation of “a non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instructions to apply the abstract idea using generic computer components. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f).
Therefore, claim 11 is ineligible.
Regarding Original Claim 13,
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 13.
Therefore, claim 13 is ineligible.
Regarding Original Claim 14,
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 14.
Therefore, claim 14 is ineligible.
Regarding Original Claim 15,
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 15.
Therefore, claim 15 is ineligible.
Regarding Original Claim 16,
The claim recites similar limitations as corresponding claim 6. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 6, as described above, is equally applicable to claim 16.
Therefore, claim 16 is ineligible.
Regarding Original Claim 17,
The claim recites similar limitations as corresponding claim 7. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 7, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Currently amended Claim 18,
The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 8, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Currently amended Claim 19,
The claim recites similar limitations as corresponding claim 9. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 9, as described above, is equally applicable to claim 19.
Therefore, claim 19 is ineligible.
Regarding Original Claim 20,
The claim recites similar limitations as corresponding claim 10. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 10, as described above, is equally applicable to claim 20.
Therefore, claim 20 is ineligible.
Claim Interpretation
The claim recites limitations that contain contingent limitations such as:
...
predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources;
in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit;
selecting an execution environment for the quantum computing function based on the predicted cuttability and the predicted number of sub-circuits;
generating an execution plan for the plurality of sub-circuits based on the predicted number of sub-circuits and hardware constraints of the available quantum computing resources; and
executing the quantum computing function in the execution environment according to the execution plan.
....
Contingent limitation:
The claim recites the prediction step on whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources, following this step the claim recites the condition of “in response to a prediction that the quantum circuit can be cut.” The conditional operation is interpreted as follows:
Condition 1: if the prediction is that the quantum circuit can be cut: the claim process requires the following steps:
predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit;
selecting an execution environment for the quantum computing function based on the predicted cuttability and the predicted number of sub-circuits;
generating an execution plan for the plurality of sub-circuits based on the predicted number of sub-circuits and hardware constraints of the available quantum computing resources; and
executing the quantum computing function in the execution environment according to the execution plan.
Condition 2: if the prediction is that the quantum circuit cannot be cut: the proceeding steps are not required to be performed.
Under MPEP § 2111.04(II) and Ex parte Schulhauser, the broadest reasonable interpretation (BRI) of a method claim with contingent steps requires only those steps whose conditions precedent is actually met. Steps whose condition is not met are not required to be performed for the claim to be implemented.
Specifically, the steps of (i) predicting a number of sub-circuits from cutting the quantum circuit, (ii) selecting an execution environment for the quantum computing function, (iii) generating an exaction plan for the sub-circuits, and (iv) executing the quantum computing function in the execution environment according to the execution plan are all conditioned on a prediction that the quantum circuit can be cut into a plurality of sub-circuits satisfying hardware requirements. If this condition is not satisfied, none of these steps is required to be performed. See MPEP § 2111.04(II).
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.
Claim(s) 1, 3, 5-6, 8-9, 11, 13, 15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Durazzo et al., (Pub. No.: US 20210406151 A1) in view of Davis et al., (Pub. No.: US 20230020389 A1), further in view of Tang et al., (NPL: "Cutqc: using small quantum computers for large quantum circuit evaluations." (2021)), and further in view of Basu et al., (NPL: "i-QER: An Intelligent Approach Towards Quantum error reduction." (2022)). Hereinafter, the combination of Durazzo, Davis, Tang, and Basu teach the following.
Regarding Currently amended Claim 1,
Durazzo discloses the following:
A method, comprising: (Durazzo, [0008] “Embodiments of the present invention generally relate to quantum computing. More particularly, at least some embodiments of the invention relate to systems, hardware, software, computer-readable media, and methods for determining, for one or more particular tasks, whether real QPUs (quantum Processing Units) or simulation should be used to carry out the task, and for allocating computing resources accordingly.” [0043] “Attention is directed now to FIG. 2, where methods are disclosed for the implementation and use of a quantum computing platform that may comprise both real QPUs and quantum simulation clusters, where one example method is denoted generally at 300.”)
predicting, using a machine learning model, runtime characteristics concerning a quantum computing function; (Durazzo, [0032] “the estimator of the runtime cluster 125, may, based on information provided to it by the quantum middleware 133, estimate one or more runtime statistics for one or more quantum computing services, that is, the services provided as a result of execution of one or more quantum circuits. Such runtime statistics may include, but are not limited to, the execution time and memory space consumption for the quantum computing services. Such estimates may be based on historical information for the same, or similar, quantum computing services.” [0036] “an estimator may alternatively employ a top-down approach which may involve the use of machine learning (ML). In one embodiment of the top-down approach, the estimator may make one or more predictions based on previously defined ML models. These ML models may be trained by historical data from experimentations. Any embodiment of an estimator, including the aforementioned examples, may provide the quantum simulation cluster 175 with at least two predictions or inputs, namely, execution time for the quantum code, and memory space requirements for execution of the quantum code.”) [Examiner’s Note: Durazzo explicitly disclose the use of a machine learning model (the top-down ML-based estimator) to predict runtime characteristics, specifically execution time and memory space requirements of a quantum computing function (i.e., a quantum computing algorithm/service).]
predicting, using the machine learning model, resources needed to perform the quantum computing function, (Durazzo, [0036] “Another input that may be generated by an estimator is processing requirements for execution of the quantum code.” [0041] “For example, some embodiments comprise an estimator that may be operable to predict runtime statistics for a quantum circuit, such as execution time and memory space required. Some embodiments may comprise a cluster orchestration engine that may be operable to dynamically allocate, and release, resources based on estimated resources required for quantum circuits, as determined by an estimator for example.” [0018] “embodiments of the invention may determine the resources required by the algorithm prior its execution, so that those resources can be allocated accurately.”) which comprises a circuit-cutting process for a quantum circuit; (Durazzo, [0046] “the recommendation that is generated 306 may indicate that execution of the quantum circuit should be split between one or more QPUs and a quantum simulation process.”) [Examiner’s Note: the ML-based estimator predict processing requirements (i.e., the resources needed) for execution of the quantum code. Under BRI, the “resources needed to perform the quantum computing function” covers the predicted processing requirements, memory space, and computational resources.]
....
[predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources;
in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit;]
...
selecting an execution environment for the quantum computing function [...] (Durazzo, [0046] “Based on the outcome of the estimating process 304, a recommendation may then be generated 306 as to whether, for example, one or more QPUs should be used to execution the quantum circuit or, alternatively, whether a quantum simulation should be employed for execution of the quantum circuit. In some embodiments, the recommendation that is generated 306 may indicate that execution of the quantum circuit should be split between one or more QPUs and a quantum simulation process.” [0037] “Using inputs, such as the two inputs from an estimator for example, a cluster orchestration module 185 of the quantum simulation cluster 175 may determine how to best execute the quantum algorithm based on available resource and user-chosen service plan... possible choices of processing resources may include, but are not limited to, QPUs, GPUs, and CPUs.”)
...
[generating an execution plan for the plurality of sub-circuits based on the predicted number of sub-circuits and hardware constraints of the available quantum computing resources;] and
...
executing the quantum computing function in the execution environment according to the execution plan.(Durazzo, [0048] “if adequate resources will be available to execute the quantum circuit, those resources may be allocated 310 for execution of the quantum circuit. Using the allocated resources, the quantum circuit may then be executed 312.” [0039] “Once the simulation cluster is in place, the simulation cluster may then execute the quantum circuit.” [0037] “Using inputs, such as the two inputs from an estimator for example, a cluster orchestration module 185 of the quantum simulation cluster 175 may determine how to best execute the quantum algorithm based on available resource and user-chosen service plan. That is, the cluster orchestration module 185 may determine how to employ processing resources in the execution of the quantum algorithm. The determination may be optimal based on the expected availability of processing resources during the time when the quantum algorithm is to be run. In some embodiments, possible choices of processing resources may include, but are not limited to, QPUs, GPUs, and CPUs.”)
Durazzo does not appear to explicitly teach the following:
predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources;
in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit;
selecting an execution environment for the quantum computing function based on the predicted cuttability and the predicted number of sub-circuits;
generating an execution plan for the plurality of sub-circuits based on the predicted number of sub-circuits and hardware constraints of the available quantum computing resources;
However, Durazzo in view of Davis teaches the following:
predicting, …. before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources; (Davis, [0062] “the hardware resource metadata can be used to identify which quantum logic circuits may be possibly executed, to estimate the corresponding runtime and associated error in the execution, or for another purpose. The hardware resource metadata can be also used in a decomposition process of the quantum logic circuit and for generating execution tasks that can be executed separately on distinct hardware resources.” [0101] “a quantum logic block from a decomposition of a quantum logic circuit may not be able to be executed on any available quantum computing resource. For example, when a number of qubits involved in a quantum logic block exceed the capacity of any available QPU or QVM. In some implementations, hardware resource metadata can be used to decompose a quantum logic circuit such that every single quantum logic block from the decomposition satisfies the hardware constraints of at least one quantum computing resource, e.g., after compilation or some other semantic-preserving transformation.”) [Examiner’s Note: Davis teaches determining whether to decompose a quantum logic circuit using hardware resource metadata that defines the hardware constraints of the quantum computing resource.]
in response to a prediction that the quantum circuit can be cut, predicting, … before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit; (Davis, [0011] “A cost-based decomposition process allows balancing of hardware compatibility, average number of executable quantum logic blocks per resource, and hardware error rates.” [0105] “ In some implementations, a cost function is expressed as below: F(B; λ)=T·c n+λ·βm (5) where B represents a decomposition of a quantum logic circuit C into k quantum logic blocks; n is the number of external connections in the decomposition B; c is an algorithm-specific constant indicating the number of execution tasks associated with a single external connection (e.g. c=8 as shown in FIG. 3 ); m is the maximum number of quantum logic operations executed in any quantum logic block; T is an estimate for the amount of work needed to execute all k quantum logic blocks across the resource set R; β is the average error per quantum operation; λ is a cost function hyperparameter which specifies a weighting of the error rate (e.g., fidelity) relative to the runtime.” Further see [0105].) [Examiner’s Note: the determined number of blocks into which a quantum circuit will be decomposed reads on the number of sub-circuits from the cutting the quantum circuit.]
selecting an execution environment for the quantum computing function based on the predicted cuttability and the predicted number of sub-circuits; (Davis, [0037] “the servers 108 can select the type of computing resource (e.g., quantum or classical) to execute an individual program, or part of a program, in the computing system 101. For example, the servers 108 may select a QPU, QVM, or other computing resource based on availability of the resource, speed of the resource, information or state capacity of the resource, a performance metric (e.g., process fidelity) of the resource, supported quantum gates, metadata indicating (in part) timing and fidelity information about these gates, or based on a combination of these and other factors. In embodiments involving QVMs, the servers 108 may select a QVM based on metadata indicating, in part, limitations on the circuit size or complexity, and timing information capturing the run-time cost of simulating circuits of various sizes.” [0057] “In such cases, the computer system 101 may utilize each quantum computing system according to the type of quantum program that is being executed.” [0108] “the execution tasks are dispatched to the respective processing node 512 based on the hardware resource metadata.” Further see [0102].)
generating an execution plan for the plurality of sub-circuits based on the predicted number of sub-circuits and hardware constraints of the available quantum computing resources; (Davis, [0031] “the servers 108 generate a schedule for executing programs, allocate computing resources in the computing system 101 according to the schedule, and delegate the programs to the allocated computing resources.” [0106]-[0107] “the scheduler module 514 is configured to generate an execution schedule for the execution tasks, e.g., by aggregating the execution tasks based on the hardware resource metadata. The execution schedule expresses a sequence of execution tasks to be performed for each processing node 512.” [0092] “an execution schedule is an assignment of execution tasks to classical or quantum computing resources (e.g., processing nodes), together with a logical or dependency ordering.”) [Examiner’s Note: the generated execution schedule based on the decomposed quantum logic blocks reads on the “execution plan.”]
executing the quantum computing function in the execution environment according to the execution plan. (Davis, [0108]-[0109] “the execution tasks are dispatched to the processing nodes 512 by the cluster server 510 according to the received execution schedule from the scheduler module 514… After receiving the execution tasks, each of the processing nodes 512 execute the received execution tasks consistent with the execution schedule”)
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art to modify the combination of Durazzo and Davis to incorporate the quantum computing task processing method as taught by Davis. One would have been motivated to make such a combination in order to increase hardware utilization, increase the quantum computational power, increase the duty cycle of the quantum computing resources, and thus reduce per unit cost of quantum computing resources during the execution of quantum logic circuits (Davis [0013]).
As outlined above, While Davis, in combination with Durazzo, uses an optimization process which takes hardware resources metadata (which includes hardware constraints of available quantum computing resources) and evaluates multiple candidate decomposition using a cost function that balances runtime and error rates, selecting the decomposition with the lowest cost, where each candidate decomposition (i.e., decomposition B) comprises k quantum logic blocks, where k represents the number of blocks. Durazzo in view of Davis does not appear to explicitly teach:
predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources;
in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit;
However, Tang, in combination with Durazzo and Davis, teaches the following:
predicting, … before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources; (Tang, [P. 4, Section: 4] “Given a quantum circuit specified as an input, the first step is to decide where to make cuts. We propose the first automatic scheme that uses mixed integer programming to find optimal cuts for arbitrary quantum circuits. The backend for the MIP cut searcher is implemented in the Gurobi solver [18]. Small quantum devices then evaluate the different combinations of the subcircuits.” [P. 5, Section: 4.1] “CutQC’s cut searcher uses mixed-integer programming to automate the identification of cuts that require the least amount of classical postprocessing. Our problem instances are solved by the Gurobi mathematical optimization solver [18]. … MIP cut searcher requires the user to specify the maximum number of qubits allowed per subcircuit, 𝐷, equal to the size of the quantum devices available to the user. Another input is the maximum-number of subcircuits allowed, 𝑛𝐶…We also require that the 𝑑𝑐 qubits in subcircuit 𝑐 be no larger than the input device size 𝐷.”) [Examiner’s Note: the MIP cut searcher identifies and evaluates the given quantum circuit before the cutting to determine the optimal solution to cut the circuit based on the given hardware constraints (e.g., the size of the quantum devices available) such that every subcircuit fits within the hardware qubit constraint. This identification reads on the claimed “predicting, before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources.”]
in response to a prediction that the quantum circuit can be cut, predicting, using the machine learning model before the circuit-cutting process is performed, a number of sub-circuits from cutting the quantum circuit; (Tang, [P. 5, Section: 4] “Figure 5: Framework overview of CutQC. A mixed-integer programming (MIP) cut searcher automatically finds optimal cuts given an input quantum circuit. The small sub circuits resulting from the cuts are then evaluated by using quantum devices. The reconstructor then reproduces the probability distributions of the original circuit.” [P. 5-6, Section: 4.1] “Choosing which edges to cut in order to split 𝐺 into subcircuits 𝐶 = 𝑐1,.. .,𝑐𝑛𝐶 can also be thought of as clustering the vertices. The corresponding cuts can then obtained from the vertex clusters… The number of cuts made is given by (see Eq (13)).” Furter see [p. 7, Section: 5.1].) [Examiner’s Note: MIP cut searcher determines an optimal solution specifying the optimal number of subcircuits and cut locations. The optimal solution is determined using the MIP cut searcher before circuit cutting see Fig. 5.]
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Durazzo, Davis, and Tang, to incorporate the CutQC approach that cuts large quantum circuits into smaller subcircuits as taught by Tang. One would have been motivated to make such a combination in order to improve the fidelity of quantum circuit executions on NISQ devices and speed up the overall quantum circuit execution (Tang [Section: 8]).
While Tang, in combination with Durazzo and Davis, teaches MIP cut searcher that identifies whether a given quantum circuit can be cut and determine the optimal number of cuts. Tang is salient on whether the MIP cut searcher a machine leaning model.
However, Basu, in combination with Durazzo, Davis, and Tang, teaches the limitation:
predicting, using the machine learning model before the circuit-cutting process is performed, whether the quantum circuit can be cut into a plurality of sub-circuits that satisfy hardware constraints of available quantum computing resources; (Basu, [P. 7, Section: 3] “The tool takes a quantum circuit as an input, and a supervised learning based error prediction system predicts the possible effects of noise on the quantum circuit when it is executed on a NISQ device [26]. If the predicted effect of noise is beyond a threshold specified by the user, the circuit is fragmented [24] into two sub-circuits using a novel Error Influenced Binary Quantum Circuit Fragmentation strategy.” [Pp. 9-10, Section: 3.2] “We cut the quantum circuit if and only if there is a cut, which satisfies the constraint on cut-size. In order to reduce the error in each partition, our approach toward fragmentation is to cut the circuit in such a way that each of the partitions share approximately equal errors.” Fig. 3, flowchart: “Circuit’s error above threshold [Wingdings font/0xE0] fragmentation into sub-circuits with cut-size K possible. [P. 14, Section: 4.2] “This circuit cannot be executed directly on the 15-qubit IBMQ Melbourne device. We have considered K ≤ 2, while finding the desired cut for fragmentation.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Durazzo, Davis, Tang, and Basu, to incorporate the proposed fragmentation process based on i-QER supervised learning models as taught by Basu. One would have been motivated to make such a combination in order to use ML-based prediction to intelligently fragment quantum circuits, reduce execution errors on NISQ hardware while minimizing unnecessary fragmentation (Basu [Section: 1]).
Regarding Original Claim 3, the combination of Durazzo, Davis, Tang, and Basu teaches the elements of claim 1 as outlined above, and further teaches:
wherein the quantum computing function comprises a quantum circuit execution. (Durazzo, [0028] “At execution time, the runtime environment may interpret programming instructions which require, or would at least benefit form, the use of quantum computing. The programming instructions may be compiled, such as by a runtime compiler of the runtime cluster 125, into a binary, or digital, version of a quantum circuit.” [0038] “In the case when the resources required for execution of quantum circuit fit within the total available resources, but some resources are consumed by another quantum circuit, the pending quantum circuit may enter a queue and return with a waiting-time estimation based on the jobs in front of it.”)
Regarding Original Claim 5, the combination of Durazzo, Davis, Tang, and Basu teaches the elements of claim 1 as outlined above, and further teaches:
wherein metadata and runtime characteristics relating to the quantum computing function are collected from the execution environment while the quantum computing function is being executed. (Davis, [0108]-[0109] “execution tasks may be dispatched to processing nodes 512 in an asynchronous fashion in operation according to the execution schedule. ... Upon execution of an execution task, a processing node 512 constructs a data buffer as an intermediate output consisting of measured bitstring values, Pauli measurement outcomes, runtime metadata (e.g. error information, execution duration, and etc.), and submits the data buffer back to the cluster server 510.” [0110] “At every logical synchronization point ... in the execution schedule, the cluster server 510 iteratively updates the quantity (e.g., EC[f (y)]) with the combined data buffer of the completed execution tasks from at least a subset of the processing nodes 512. When the cluster server 510 receives the data buffers from the processing nodes 512, it may dispatch new execution tasks to the respective processing nodes 512.”) [Examiner’ Note: Davis discloses that upon execution of a quantum execution task, each processing node constructs a data buffer containing runtime metadata (error information and execution time) and submits it back to the cluster server, which corresponds to the claimed “metadata and runtime characteristics collected while the quantum computing function is executed. The process described as these buffers are collected iteratively at logical synchronization points while execution tasks continue running across nodes.]
Regarding Original Claim 6, the combination of Durazzo, Davis, Tang, and Basu teaches the elements of claim 1 as outlined above, and further teaches:
wherein the predicting of the runtime characteristics and/or the predicting of the resources, by the machine learning model, are performed based on inputs comprising any one or more of hybrid algorithm, service level objective, simulation engine, and available hardware. (Durazzo, [0036]-[0037] “Any embodiment of an estimator, including the aforementioned examples, may provide the quantum simulation cluster 175 with at least two predictions or inputs, namely, execution time for the quantum code, and memory space requirements for execution of the quantum code. Another input that may be generated by an estimator is processing requirements for execution of the quantum code. Using inputs, such as the two inputs from an estimator for example, a cluster orchestration module 185 of the quantum simulation cluster 175 may determine how to best execute the quantum algorithm based on available resource and user-chosen service plan. That is, the cluster orchestration module 185 may determine how to employ processing resources in the execution of the quantum algorithm. The determination may be optimal based on the expected availability of processing resources during the time when the quantum algorithm is to be run.” [0041] “An embodiment of the invention may provide for hybrid-cloud orchestration for quantum compute processes. For example, by having de-coupled a container orchestration environment and quantum computing execution environment, a hybrid or multi-cloud orchestration model may be possible.”)
Regarding Claim 8, the combination of Durazzo, Davis, Tang, and Basu teach the elements of claim 1 as outlined above, and further teaches:
wherein the runtime characteristics concerning the circuit cutting process comprise a prediction as to how many sub-circuits can be created from a circuit that is a subject of the circuit cutting process. (Davis, [0068] “The quantum logic circuit 202 can be decomposed into multiple (k) quantum logic blocks 206 by performing a circuit decomposition process such that a subset of quantum logic operations in the quantum logic circuit 202 are grouped together in a single quantum logic block 206.” [0100] “the hardware resource metadata is used by the scheduler module 514 in processes of decomposing the quantum logic circuit and generating execution tasks... , hardware resource metadata can be used to decompose a quantum logic circuit such that every single quantum logic block from the decomposition satisfies the hardware constraints of at least one quantum computing resource..” [0103] “a decomposition of a quantum logic circuit C into k quantum logic blocks; n is the number of external connections in the decomposition B; c is an algorithm-specific constant indicating the number of execution tasks associated with a single external connection (e.g. c=8 as shown in FIG. 3 ); m is the maximum number of quantum logic operations executed in any quantum logic block; T is an estimate for the amount of work needed to execute all k quantum logic blocks across the resource set R; ...”) Further see [0086] and [0106].
Regarding Claim 9, the combination of Durazzo, Davis, Tang, and Basu teach the elements of claim 1 as outlined above, and further teaches:
wherein the predicting of the runtime characteristics comprises predicting that the circuit cutting process can be performed. (Davis, [0101]-[0102] “a quantum logic block from a decomposition of a quantum logic circuit may not be able to be executed on any available quantum computing resource. For example, when a number of qubits involved in a quantum logic block exceed the capacity of any available QPU or QVM... hardware resource metadata can be used to decompose a quantum logic circuit such that every single quantum logic block from the decomposition satisfies the hardware constraints of at least one quantum computing resource” [0100] “the scheduler module 514 further obtains hardware resource metadata specifying properties of the processing nodes 512 in the computing system, e.g., a number of qubits available on the respective processing nodes 512; timing information of the .. quantum computing resources of the processing nodes 512,..., error rate information and availability information of the respective processing nodes 512, and other properties..” [0062] “ the hardware resource metadata can be used to identify which quantum logic circuits may be possibly executed, to estimate the corresponding runtime and associated error in the execution, or for another purpose. The hardware resource metadata can be also used in a decomposition process of the quantum logic circuit and for generating execution tasks that can be executed separately on distinct hardware resources.” Further See [0098].) [Examiner’s Note: Davis expressly discloses that before proceeding with circuit decomposition and execution, the scheduler obtains hardware resources metadata to assess whether the circuit can be executed, specifically, whether the quantum logic blocks resulting from the decomposition satisfy the hardware constraints of quantum computing resource. Thus, the evaluation of hardware resources metadata and execution time to determine whether the circuit cutting process can proceed reads on predicting that the circuit cutting process can be performed.]
Regarding Currently amended Claim 11,
The claim recites substantially similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a method, and claim 11 is directed to a non-transitory storage medium.
Durazzo also discloses non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations. (Durazzo, [0061] “Embodiment 12. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising the operations of any one or more of embodiments 1 through 11.”)
Regarding Original Claim 13,
The claim recites substantially similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Original Claim 15,
The claim recites substantially similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding Original Claim 16,
The claim recites substantially similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding Currently amended Claim 18,
The claim recites substantially similar limitations as corresponding claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Regarding Currently amended Claim 19,
The claim recites substantially similar limitations as corresponding claim 9 and is rejected for similar reasons as claim 9 using similar teachings and rationale.
Claim(s) 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Durazzo, Davis, Tang, and Basu as outlined above, and further in view of Gambetta et al., (Pub. No.: US 20200342347 A1).
Regarding Original Claim 4,
the combination of Durazzo, Davis, Tang, and Basu teaches the elements of claim 1 as outlined above:
While Durazzo teaches that ML models are trained on historical data based on generated data from executing quantum computing operations, the combination of Durazzo, Davis, Tang, and Basu does not appear to explicitly teach:
wherein telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself.
However, it would have been obvious in view of Gambetta. Hereinafter, Gambetta, in combination with Durazzo, Davis, Tang, and Basu, teaches:
wherein telemetry concerning execution of another quantum computing function is used by the machine learning model to retrain itself. (Gambetta, [0081] “the validated quantum circuit 420 fails execution on the quantum processor. Quantum processing system 140 returns the failed quantum circuit execution 422 to the classical processing system 104. In an embodiment, the failed quantum circuit execution 422 includes a state of the quantum processor 142, a reason for execution failure of the quantum circuit, and the corresponding quantum circuit. Component 404 receives the failed quantum circuit execution 422 and updates the set of quantum circuits 416A and the set of quantum processor states 416B with the corresponding quantum circuit and the state of the quantum processor. Component 404 can then retrain the machine learning model with the updated data set.” [0083] “Classical processor 502 receives failed quantum circuit, the state of the quantum processor, and the reason for execution failure from quantum processor 304 and updates a training data set for the machine learning algorithm and retrains the machine learning model with the returned data.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Durazzo, Davis, Tang, Basu, and Gambetta, to incorporate the machine learning quantum algorithm validator as taught by Gambetta. One would have been motivated to make such a combination in order to provide a circuit optimization software for conventional circuits that significantly reduces resource demands, thereby increasing efficiency and decreasing complexity (Gambetta [0011]).
Regarding Original Claim 14,
The claim recites substantially similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Claim(s) 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Durazzo, Davis, Tang, and Basu as outlined above, and further in view of Seth et al., (Pub. No.: US 20220188697 A1).
Regarding Original Claim 7, the combination of Durazzo, Davis, Tang, and Basu teaches the elements of claim 1 as outlined above:
the combination of Durazzo, Davis, Tang, and Basu does not appear to explicitly teach:
wherein the resources are used to retrain the machine learning model when demand for those resources permits.
However, it would have been obvious in view of Seth. Hereinafter, Seth, in combination with Durazzo, Davis, Tang, and Basu, teaches:
wherein the resources are used to retrain the machine learning model when demand for those resources permits. (Seth, [0041]-[0042] “...excess capacity may be used for retraining. For instance, if computing system(s) both host and retrain AI/ML models, the computing system(s) may use idle processors (e.g., GPUs and/or CPUs) for retraining, which speeds up the retraining process as fewer hardware resources are consumed by the currently serving AI/ML models. Conversely, in some embodiments, processing resources may be allocated away from retraining when demand for execution of currently serving AI/ML models and/or other services increases..” Further see [0079] and [0088].)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective date of the claimed invention, having the combination of Durazzo, Davis, Tang, Basu, and Seth to incorporate the method for AI/ML model retraining hardware control as taught by Seth. One would have been motivated to make such a combination in order to automatic balancing and utilizing resources effectively for AI/ML model, thereby providing an improved approach to AI/ML model retraining and management (Seth [0003]).
Regarding Original Claim 17,
The claim recites substantially similar limitations as corresponding claim 7 and is rejected for similar reasons as claim 7 using similar teachings and rationale.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Durazzo, Davis, Tang, and Basu as outlined above, and further in view of Kohagen et al., (Pub. No.: US 20250148339 A1).
Regarding Original Claim 10,
the combination of Durazzo, Davis, Tang, and Basu teaches the elements of claim 1 as outlined above:
While Durazzo teaches that ML models are trained on historical data based on generated data from executing quantum computing operations, the combination of Durazzo, Davis, Tang, and Basu does not appear to explicitly teach:
wherein the machine learning model is trained in real-time as telemetry is received concerning execution of another quantum computing function.
However, it would have been obvious in view of Kohagen. Hereinafter, Kohagen, in combination with Durazzo, Davis, Tang, and Basu, teaches:
wherein the machine learning model is trained in real-time as telemetry is received concerning execution of another quantum computing function. (Kohagen, [0085]-[0086]“Starting at step/operation 202, operational data for a particular quantum processor is obtained... the controller 30 and/or computing entity 10 may cause the particular quantum processor 115 to perform one or more calibration processes and/or to execute at least a portion of a quantum circuit and receive the operational data generated as a result of and/or during the performance of the one or more calibration processes and/or execution of the at least a portion of the quantum circuit... One or more modules of the quantum noise decoder then use the operational data to train the machine-learning based quantum error determination model and to generate a noise model characterizing the noise of the particular quantum processor 115...” [0095] “Starting at step/operation 302, circuit performance data generated during operation of the particular quantum processor is received. [0107] “the machine-learning technique and/or process is iterative such that continued training of the quantum error determination model 420 is performed as new (empirical) operational data is generated (e.g., through operation of the particular quantum processor) and/or provided to the quantum noise decoder 400.” [0111] “a machine learning technique may be used to train the quantum error determination model 420 using training data that includes empirical operational data corresponding to the operation of the particular quantum processor 115... he training may be a continued training of an already trained quantum error determination model 420 (e.g., using a new batch of training data comprising empirical operational data), where the initial weights of the one or more DNNs are set to previously trained values.” Further described in paragraphs [0058], [0143], and [0151].) [Examiner’s Note: the operational data obtained during execution reads on the telemetry.]
Accordingly, it would have been prima facie obvious to one having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Durazzo, Davis, Tang, Basu, and Kohagen, to incorporate the techniques for managing noise and determining errors in a quantum computing process as taught by Kohagen. One would have been motivated to provide technical solutions and technical advantages to quantum computing, including the fields of quantum error correction, real-time quantum error correction, quantum processor noise reduction, and/or the like (Kohagen [0060]).
Regarding Original Claim 20,
The claim recites substantially similar limitations as corresponding claim 10 and is rejected for similar reasons as claim 10 using similar teachings and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(Pub. No.: US 20240061724 A1) – “Dongyi Zhao” relates to “Quantum computing task execution method and apparatus, and quantum computer operating system.”
[0014]-[0015] “the determining the target number according to a preset dividing unit and/or the number of current idle processes includes: calculating a number of first subcircuits corresponding to the target quantum circuit, wherein the target quantum circuit is divided according to the preset dividing unit to obtain the number of first subcircuits; and determining a maximum value between the number of first subcircuits and the number of current idle processes as the target number… the dividing the target quantum circuit into a target number of subcircuits according to a preset dividing rule includes: acquiring a number of current idle processes, wherein the number of the current idle processes equals to a number of currently callable query processes; and determining the target number according to a preset dividing unit and/or the number of current idle processes, and dividing the target quantum circuit into the target number of subcircuits.”
(Pub. No.: US 20190095561 A1) – “Edwin Peter Dawson Pednault” relates to “Simulating quantum circuits.”
FIG. 10 is a flowchart depicting one example of a quantum circuit partitioning method in accordance with at least one embodiment of the present invention.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SADIK ALSHAHARI whose telephone number is (703)756-4749. The examiner can normally be reached Monday Friday, 9 A.M - 6 P.M. ET..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached on (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/S.A.A./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
Prosecution Insights