DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to the amendment filed on 09/09/2025. By this amendment, Claims 1, 6, 8, 11, 16, and 18 have been amended. Claims 3 and 13 have been cancelled. Therefore, Claims 1-2, 4-12, and 14-20 are pending for examination.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/11/2022, 08/09/2023, 11/09/2023, and 10/25/2024 are in compliance with the provisions of 37 CFR 1.97, and are being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-7, 9-12, 14-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dukatz et al. US 20180308000 A1 (Dukatz) in view of Acampora et al. “A dataset for quantum circuit mapping,” Data in Brief, Volume 39, 2021, 107526, ISSN 2352-3409 (Acampora, published October 29 2021).
Acampora was cited in IDS filed on 11/11/2022.
Regarding claim 1, Dukatz teaches A method comprising: inputting features of a quantum workload into a machine learning model that has been trained to estimate at least transpilation metrics; (Fig. 4, 402; [0115-0116]: “The system receives data representing a computational task to be performed by a system including one or more quantum computing resources, e.g., one or more quantum gate computers, adiabatic annealers, or quantum simulators, and one or more classical computing resources, e.g., one or more classical computers or super computers (step 402). The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404).”; Examiner notes, though Dukatz does not explicitly teach “transpilation metrics”, it is suggested as [0009] discusses several of the properties to consider when mapping a computational task to a quantum computing resource. Additionally, a task running on a quantum computing resource is a quantum task.)
receiving an output from the machine learning model; selecting a quantum system from among the available quantum systems based on the output of the machine learning model; (Fig. 4 – 404, 406; [0116-0118]: “The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404)… The system routes the data representing the computational task to the determined computing resource to obtain, from the determined computing resource, data representing a solution to the computational task (step 406).”; [0089]: “During operation (B), the machine learning model 204 is configured to provide the determined additional computing resource or resources with instructions for performing the respective computational task or computational sub tasks, e.g., data 208. For example, the machine learning model 204 may determine that a received optimization task should be routed to a quantum annealer, e.g., quantum annealer 110a. In this example, during operation (B), the machine learning model 204 may provide the quantum annealer 110a with instructions for performing the optimization task.”; Please note that a quantum computing resource being selected, i.e., determining which of the quantum computing resources to route the data representing the computational task to corresponds to Applicant’s selecting a quantum system from among the available quantum systems based on the output of the machine learning model, as the machine learning model performs the determination, corresponding to receiving an output from the machine learning model.);
and executing the quantum workload using the quantum system that was selected (Fig. 4 – 404, 406; [0116-0118]: “The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404)… The system routes the data representing the computational task to the determined computing resource to obtain, from the determined computing resource, data representing a solution to the computational task (step 406).”; [0089]: “During operation (B), the machine learning model 204 is configured to provide the determined additional computing resource or resources with instructions for performing the respective computational task or computational sub tasks, e.g., data 208. For example, the machine learning model 204 may determine that a received optimization task should be routed to a quantum annealer, e.g., quantum annealer 110a. In this example, during operation (B), the machine learning model 204 may provide the quantum annealer 110a with instructions for performing the optimization task.”; Examiner notes, when a quantum computing resource is selected, data representing a solution being returned to the task from the resource means the workload was executed on the quantum computing resource.);
Dukatz does not teach transpilation metrics;
wherein the output includes a transpilation time and an estimated circuit execution time for each quantum system available for the quantum workload
However, in analogous art, Acampora teaches transpilation metrics. (Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, “transpilation metrics” are understood in light of [0008-0009] of the instant specification, being metrics related to mapping an appropriate quantum circuit to a quantum processor.)
wherein the output includes a transpilation time (Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would accept the same metrics as parameters in input data.)
and an estimated circuit execution time for each quantum system available for the quantum workload (Pg. 3: “CXETi,j - an array of real values where each value represents the execution time (in nanoseconds) of a CNOT gate using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P”; Examiner notes, “and/or” is understood as ’A’ or ‘B’ or both).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the transpilation metrics, such as where the output includes a transpilation time and an estimated circuit execution time for each quantum system available for the quantum workload, in Acampora with the training data reported to the machine learning model in Dukatz, allowing Dukatz’s model to consider such transpilation metrics when finding an appropriate quantum resource to run the task on. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success to overcome the limitations of current quantum algorithms for circuit to processor mapping as stated in the abstract of Acampora: “…a major limitation in the design of a quantum algorithm is related to the proper mapping of the corresponding circuit to a specific quantum processor so that the underlying physical constraints are satisfied. Moreover, current deterministic mapping algorithms suffer from high run times as the number of qubits to map increases. To bridge the gap in view of the next generation of quantum computers composed of thousands of qubits, this data paper proposes the first datasets that help address the quantum circuit mapping problem as a classification task.” Examiner notes, though Acampora presents his findings as datasets, the process for acquiring such data and metrics are provided on pg. 5-6.
Regarding claim 2, Dukatz in view of Acampora teaches the method of claim 1.
Dukatz in view of Acampora teaches wherein the features include parameters of a transpilation algorithm, (Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would accept the same metrics as parameters in input data.)
features of a quantum system, (Fig. 2 – 206, 212; [0106]: “The system obtains a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks”)
and features of the quantum workload. (Fig. 2 – 102; [0044]: “The system 100 for performing computational tasks is configured to receive as input data representing a computational task to be solved, e.g., input data 102.”; Examiner notes, a task assigned to run on a quantum computing resource is a quantum task).
Regarding claim 4, Dukatz in view of Acampora teaches the method of claim 1.
Dukatz further teaches further comprising selecting the quantum system based on whether the output is within a tolerance of service level objective constraints. ([0046]: “In some implementations, the input data 102 may further include data specifying one or more task objectives associated with the computational task. The task objectives may include local task objectives and global task objectives. Local task objectives may include local targets to be considered when solving the computational task, e.g., local objectives of a solution to the computational task. For example, local objectives may include constraints on values of subsets of computational task variables.”; [0047]: “For example, continuing the above example of the task of optimizing a water network, the input data 102 may further include data specifying local task objectives such as a constraint on the concentration of chemicals in the water, e.g., constraining the chemical concentration to between 0.2% and 0.5%” Examiner notes, a tolerance level).
Regarding claim 5, Dukatz in view of Acampora teaches the method of claim 4.
Dukatz in view of Acampora further teaches wherein the tolerance is defined as a distance between the output and a ground truth. (Dukatz, [0047]: “For example, continuing the above example of the task of optimizing a water network, the input data 102 may further include data specifying local task objectives such as a constraint on the concentration of chemicals in the water, e.g., constraining the chemical concentration to between 0.2% and 0.5%”; Examiner notes, Dukatz in view of Acampora is able to consider, as an input in the model, a service level objective (ground truth as viewed in light of [0043] of instant specification) when choosing if a resource will be within the given tolerance. Therefore, Dukatz in view of Acampora would be measuring a distance between the output and a service level objective given Dukatz in view of Acampora determines if the output is within the tolerance of a service level objective constraint).
Regarding claim 6, Dukatz in view of Acampora the method of claim 1.
Dukatz in view of Acampora further teaches comprising inputting the features into multiple machine learning models, (Dukatz, [0082]: “The machine learning module 132 will support multiple software for the machine learning models 204 based on the environment scale and runtime”)
wherein each of the machine learning models is associated with a different transpilation algorithm. (Acampora, Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, Dukatz in view of Acampora would utilize Acampora’s metrics in the model in Dukatz as previously combined, therefore the output of the machine learning model would be associated with the metrics.)
Regarding claim 7, Dukatz in view of Acampora teaches the method of claim 1.
Dukatz further teaches comprising training the machine learning model. (Fig. 3; [0083]: “The machine learning module 132 is configured to train the machine learning model 204 to route computations or sub-computations received by the machine learning module 132 to the one or more additional computing resources 110a-110d.”)
Regarding claim 9, Dukatz in view of Acampora teaches the method of claim 1.
Dukatz further teaches comprising looping through multiple configurations to select the quantum system, (Fig. 1A – 110a-110c; [0057]: “For example, the computation engine 106 may be configured to analyze the received input data 102 representing the computational task to be solved and the data representing corresponding task objectives 112a and 112b, and outsource one or more computations associated with solving the computational task based on the task objectives 112a and 112b to the additional computing resources 110a-110d.”; [0058]: “The additional computing resources 110a-110d may include quantum annealer computing resources, e.g., quantum annealer 110a.”; [0059]: “The additional computing resources 110a-110d may include one or more quantum gate processors, e.g., quantum gate processor 110b. A quantum gate processor includes one or more quantum circuits, i.e., models for quantum computation in which a computation is performed using a sequence of quantum logic gates, operating on a number of qubits (quantum bits).”; [0060]: “The additional computing resources 110a-110d may include one or more quantum simulators, e.g., quantum simulator 110c. A quantum simulator is a quantum computer that may be programmed to simulate other quantum systems and their properties.”)
wherein the quantum system is a first quantum system to satisfy a tolerance. ([0046]: “In some implementations, the input data 102 may further include data specifying one or more task objectives associated with the computational task...For example, local objectives may include constraints on values of subsets of computational task variables.”; [0047]: “For example, continuing the above example of the task of optimizing a water network, the input data 102 may further include data specifying local task objectives such as a constraint on the concentration of chemicals in the water, e.g., constraining the chemical concentration to between 0.2% and 0.5%.” Examiner notes, various quantum systems are considered and the system that satisfies the required constraints is chosen for the computational task).
Regarding claim 10, Dukatz in view of Acampora teaches the method of claim 1.
Dukatz in view of Acampora further teaches comprising training the machine learning model to learn a relationship between input features (Dukatz, Fig. 3; [0083]: “The machine learning module 132 is configured to train the machine learning model 204 to route computations or sub-computations received by the machine learning module 132 to the one or more additional computing resources 110a-110d.”) related to a quantum circuit, (Dukatz, [0059]: “A quantum gate processor includes one or more quantum circuits”)
target quantum systems, (Dukatz, [0106]: “The system obtains a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks (step 306).”; Examiner notes, Dukatz in view of Acampora considers quantum circuits a part of other quantum computing resources when reporting data to train the machine learning model.)
and transpilation metrics to output metrics of a transpiled quantum circuit. (Acampora, Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would accept the same metrics as parameters in input data and when outputting data).
Regarding claim 11, Dukatz teaches A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising: ([0120]: “Implementations of the digital and/or quantum subject matter described in this specification can be implemented as one or more digital and/or quantum computer programs, i.e., one or more modules of digital and/or quantum computer program instructions encoded on a tangible non-transitory storage medium for execution by, or to control the operation of, data processing apparatus.”)
inputting features of a quantum workload into a machine learning model that has been trained to estimate at least transpilation metrics; (Fig. 4, 402; [0115-0116]: “The system receives data representing a computational task to be performed by a system including one or more quantum computing resources, e.g., one or more quantum gate computers, adiabatic annealers, or quantum simulators, and one or more classical computing resources, e.g., one or more classical computers or super computers (step 402). The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404).”; Examiner notes, though Dukatz does not explicitly teach “transpilation metrics”, it is suggested as [0009] discusses several of the properties to consider when mapping a computational task to a quantum computing resource. Additionally, a task running on a quantum computing resource is a quantum task.)
receiving an output from the machine learning model; selecting a quantum system from among the available quantum systems based on the output of the machine learning model; (Fig. 4 – 404, 406; [0116-0118]: “The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404)… The system routes the data representing the computational task to the determined computing resource to obtain, from the determined computing resource, data representing a solution to the computational task (step 406).”; [0089]: “During operation (B), the machine learning model 204 is configured to provide the determined additional computing resource or resources with instructions for performing the respective computational task or computational sub tasks, e.g., data 208. For example, the machine learning model 204 may determine that a received optimization task should be routed to a quantum annealer, e.g., quantum annealer 110a. In this example, during operation (B), the machine learning model 204 may provide the quantum annealer 110a with instructions for performing the optimization task.”; Please note that a quantum computing resource being selected, i.e., determining which of the quantum computing resources to route the data representing the computational task to corresponds to Applicant’s selecting a quantum system from among the available quantum systems based on the output of the machine learning model, as the machine learning model performs the determination, corresponding to receiving an output from the machine learning model.);
and executing the quantum workload using the quantum system that was selected (Fig. 4 – 404, 406; [0116-0118]: “The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404)… The system routes the data representing the computational task to the determined computing resource to obtain, from the determined computing resource, data representing a solution to the computational task (step 406).”; [0089]: “During operation (B), the machine learning model 204 is configured to provide the determined additional computing resource or resources with instructions for performing the respective computational task or computational sub tasks, e.g., data 208. For example, the machine learning model 204 may determine that a received optimization task should be routed to a quantum annealer, e.g., quantum annealer 110a. In this example, during operation (B), the machine learning model 204 may provide the quantum annealer 110a with instructions for performing the optimization task.”; Examiner notes, when a quantum computing resource is selected, data representing a solution being returned to the task from the resource means the workload was executed on the quantum computing resource.);
Dukatz does not teach transpilation metrics;
wherein the output includes a transpilation time and an estimated circuit execution time for each quantum system available for the quantum workload
However, in analogous art, Acampora teaches transpilation metrics. (Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, “transpilation metrics” are understood in light of [0008-0009] of the instant specification, being metrics related to mapping an appropriate quantum circuit to a quantum processor.)
wherein the output includes a transpilation time (Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would accept the same metrics as parameters in input data.)
and an estimated circuit execution time for each quantum system available for the quantum workload (Pg. 3: “CXETi,j - an array of real values where each value represents the execution time (in nanoseconds) of a CNOT gate using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P”; Examiner notes, “and/or” is understood as ’A’ or ‘B’ or both).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the transpilation metrics, such as where the output includes a transpilation time and an estimated circuit execution time for each quantum system available for the quantum workload, in Acampora with the training data reported to the machine learning model in Dukatz, allowing Dukatz’s model to consider such transpilation metrics when finding an appropriate quantum resource to run the task on. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success to overcome the limitations of current quantum algorithms for circuit to processor mapping as stated in the abstract of Acampora: “…a major limitation in the design of a quantum algorithm is related to the proper mapping of the corresponding circuit to a specific quantum processor so that the underlying physical constraints are satisfied. Moreover, current deterministic mapping algorithms suffer from high run times as the number of qubits to map increases. To bridge the gap in view of the next generation of quantum computers composed of thousands of qubits, this data paper proposes the first datasets that help address the quantum circuit mapping problem as a classification task.” Examiner notes, though Acampora presents his findings as datasets, the process for acquiring such data and metrics are provided on pg. 5-6.
Regarding claim 12, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 11.
Dukatz in view of Acampora further teaches wherein the features include parameters of a transpilation algorithm, (Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previous combined, therefore would accept the same metrics as parameters in input data.)
features of a quantum system, (Fig. 2 – 206, 212; [0106]: “The system obtains a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks”)
and features of the quantum workload. (Fig. 2 – 102; [0044]: “The system 100 for performing computational tasks is configured to receive as input data representing a computational task to be solved, e.g., input data 102.”; Examiner notes, a task assigned to run on a quantum computing resource is a quantum task).
Regarding claim 14, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 11.
Dukatz further teaches comprising selecting the quantum system based on whether the output is within a tolerance of service level objective constraints. ([0046]: “In some implementations, the input data 102 may further include data specifying one or more task objectives associated with the computational task. The task objectives may include local task objectives and global task objectives. Local task objectives may include local targets to be considered when solving the computational task, e.g., local objectives of a solution to the computational task. For example, local objectives may include constraints on values of subsets of computational task variables.”; [0047]: “For example, continuing the above example of the task of optimizing a water network, the input data 102 may further include data specifying local task objectives such as a constraint on the concentration of chemicals in the water, e.g., constraining the chemical concentration to between 0.2% and 0.5%” Examiner notes, a tolerance level).
Regarding claim 15, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 14.
Dukatz in view of Acampora further teaches wherein the tolerance is defined as a distance between the output and a ground truth. (Dukatz, [0047]: “For example, continuing the above example of the task of optimizing a water network, the input data 102 may further include data specifying local task objectives such as a constraint on the concentration of chemicals in the water, e.g., constraining the chemical concentration to between 0.2% and 0.5%”; Examiner notes, Dukatz in view of Acampora is able to consider, as an input in the model, a service level objective (ground truth as viewed in light of [0043] of instant specification) when choosing if a resource will be within the given tolerance. Therefore, Dukatz in view of Acampora would be measuring a distance between the output and a service level objective given Dukatz in view of Acampora determines if the output is within the tolerance of a service level objective constraint).
Regarding claim 16, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 11.
Dukatz in view of Acampora further teaches comprising inputting the features into multiple machine learning models, (Dukatz, [0082]: “The machine learning module 132 will support multiple software for the machine learning models 204 based on the environment scale and runtime”)
wherein each of the machine learning models is associated with a different transpilation algorithm. (Acampora, Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Further, Dukatz in view of Acampora would utilize Acampora’s metrics in the model in Dukatz as previously combined, therefore the output of the machine learning model would be associated with the metrics.)
Regarding claim 17, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 11.
Dukatz further teaches comprising training the machine learning model. (Fig. 3; [0083]: “The machine learning module 132 is configured to train the machine learning model 204 to route computations or sub-computations received by the machine learning module 132 to the one or more additional computing resources 110a-110d.”)
Regarding claim 19, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 11.
Dukatz further teaches comprising looping through multiple configurations to select the quantum system, (Fig. 1A – 110a-110c; [0057]: “For example, the computation engine 106 may be configured to analyze the received input data 102 representing the computational task to be solved and the data representing corresponding task objectives 112a and 112b, and outsource one or more computations associated with solving the computational task based on the task objectives 112a and 112b to the additional computing resources 110a-110d.”; [0058]: “The additional computing resources 110a-110d may include quantum annealer computing resources, e.g., quantum annealer 110a.”; [0059]: “The additional computing resources 110a-110d may include one or more quantum gate processors, e.g., quantum gate processor 110b. A quantum gate processor includes one or more quantum circuits, i.e., models for quantum computation in which a computation is performed using a sequence of quantum logic gates, operating on a number of qubits (quantum bits).”; [0060]: “The additional computing resources 110a-110d may include one or more quantum simulators, e.g., quantum simulator 110c. A quantum simulator is a quantum computer that may be programmed to simulate other quantum systems and their properties.”)
wherein the quantum system is a first quantum system to satisfy a tolerance. ([0046]: “In some implementations, the input data 102 may further include data specifying one or more task objectives associated with the computational task...For example, local objectives may include constraints on values of subsets of computational task variables.”; [0047]: “For example, continuing the above example of the task of optimizing a water network, the input data 102 may further include data specifying local task objectives such as a constraint on the concentration of chemicals in the water, e.g., constraining the chemical concentration to between 0.2% and 0.5%.” Examiner notes, various quantum systems are considered and the system that satisfies the required constraints is chosen for the computational task).
Regarding claim 20, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 11.
Dukatz in view of Acampora further teaches comprising training the machine learning model to learn a relationship between input features (Dukatz, Fig. 3; [0083]: “The machine learning module 132 is configured to train the machine learning model 204 to route computations or sub-computations received by the machine learning module 132 to the one or more additional computing resources 110a-110d.”)
related to a quantum circuit, (Dukatz, [0059]: “A quantum gate processor includes one or more quantum circuits”)
target quantum systems, (Dukatz, [0106]: “The system obtains a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks (step 306).”; Examiner notes, Dukatz in view of Acampora considers quantum circuits a part of other quantum computing resources when reporting data to train the machine learning model.)
and transpilation metrics to output metrics of a transpiled quantum circuit. (Acampora, Pg. 3: “Considering this, there is a strong demand for quantum compilers able to identify efficient initial mapping among circuit qubits and processor qubits, so as to optimize in a reasonable amount of time the number of SWAP operations required to execute the compiled circuit. To bridge this gap this paper proposes the firsts dataset useful to address the quantum circuit mapping problem as classification task.”; Examiner notes, at least pg. 3-4 contain various transpilation metrics that are obtained from the quantum circuits and quantum processors using the Qiskit library. Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would accept the same metrics as parameters in input data and when outputting data).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dukatz et al. US 20180308000 A1 (Dukatz) in view of Acampora et al. “A dataset for quantum circuit mapping,” Data in Brief, Volume 39, 2021, 107526, ISSN 2352-3409 (Acampora), further in view of Gunnels et al. US 20210240893 A1 (Gunnels).
Regarding claim 8, Dukatz in view of Acampora teaches the method of claim 7.
Dukatz in view of Acampora further teaches comprising: selecting target quantum systems, (Dukatz, Fig. 4 – 404, 406; [0116-118]: “The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404))
wherein features including one or more of: a connectivity matrix, (Acampora, Pg. 5: “In this section the data acquisition procedure is discussed. All the dataset have been collected using Python 1 : in detail, for each of them, an empty pandas dataframe was filled in with the procedure hereafter described. Such process makes intensive use of the Qiskit 2 library [3].” Examiner notes, previous and following metrics are stored in a dataframe object which is a matrix.)
a connectivity matrix enriched with cross talk information between qubits, expected execution time of each qubit, (Acampora, Pg. 3: “CXETi,j - an array of real values where each value represents the execution time (in nanoseconds) of a CNOT gate using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P.”; Examiner notes, as the CNOT gate uses qubits, measuring the execution time of the CNOT gate is also measuring the execution time of each qubit.)
a connectivity matrix enriched with CNOT error rates between each pair of qubits, (Acampora, Pg. 3: CXERi,j - an array of real values where each value represents the error rate of a CNOT using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P.”)
a connectivity matrix enriched with CNOT execution times between each pair of qubits, (Acampora, Pg. 3: “CXETi,j - an array of real values where each value represents the execution time (in nanoseconds) of a CNOT gate using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P.”)
a transverse relaxation time, (Acampora, Pg. 4: “T2 – an array of real values where each value represents the transverse relaxation time in micro-seconds ( T2i ) characterizing a qubit qi of the processor P.”)
a longitudinal relaxation time, (Acampora, Pg. 4: “T1 - an array of real values where each value represents the longitudinal relaxation time ( T1i ) in micro-seconds characterizing a qubit qi of the processor P.”)
and a readout error are collected for each of the target quantum systems; (Acampora, Pg. 3: “ER0 an array of real values where each value ER0i represents the readout error characterizing a qubit qi of the processor P.”)
generating random quantum circuits (Acampora, Pg. 5: “Generation of a random quantum circuit using the generator provided by Qiskit: in detail, this step is useful to create random quantum circuits composed of single and multi-qubits logical gates.”)
and collecting transpilation metrics for the random quantum circuits for each of the selected target quantum systems; (Acampora, Pg. 6: “At this point, the total number of controlled not quantum gates Ncx is collected together with the matrix Ncx, whose item [i, j] contains the number of CX gates between the ith and the jth qubit of the circuit where the further is the control qubit and the latter the target one. Ncx and Ncxi,j are inserted in the dataframe”; “The calibration data contains all the features related to the processor P described in previous section, that are therefore collected and inserted in the pandas dataframe”; Examiner notes, “previous section” refers to the metrics previously cited and also on pg. 3-4. )
and including the collected features and the transpilation metrics in a training dataset. (Dukatz, Fig. 3 – 306; [0106]: “The system obtains a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks (step 306).”; Examiner notes, Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would report the metrics to the machine learning module for training).
Dukatz in view of Acampora does not teach cross talk information between qubits.
However, in analogous art, Gunnels teaches cross talk information between qubits ([0081]: “Component 410 analyzes a set of quantum gate parameters...In an embodiment, calibration operation 412 returns a set of quantum gate parameters values for a plurality of quantum gates of the quantum processor 414. For example, gate error rates, gate speeds, gate cross talk matrix, and other quantum gate parameter values can be determined by the calibration operation”).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the reporting of cross talk error information from Gunnels with the systems and methods of Dukatz in view of Acampora, allowing the data containing properties associated with quantum computing resources in Dukatz in view of Acampora to also contain cross talk information. Therefore, along with the other metrics from Dukatz in view of Acampora, the cross talk information from Gunnels may also be used to train models when reported to the machine learning module and considered in the output from the machine learning module. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, to be able to track unwanted interactions between qubits on the quantum computing resources, thus being able to improve measuring how a task’s accuracy will be affected (see at least Gunnels, [0015]).
Regarding claim 18, Dukatz in view of Acampora teaches the non-transitory storage medium of claim 17.
Dukatz in view of Acampora further teaches comprising: selecting target quantum systems, (Dukatz, Fig. 4 – 404, 406; [0116-118]: “The system processes the received data using a machine learning model to determine which of the one or more quantum computing resources or the one or more classical computing resources to route the data representing the computational task to (step 404))
wherein features including one or more of: a connectivity matrix, (Acampora, Pg. 5: “In this section the data acquisition procedure is discussed. All the dataset have been collected using Python 1 : in detail, for each of them, an empty pandas dataframe was filled in with the procedure hereafter described. Such process makes intensive use of the Qiskit 2 library [3].” Examiner notes, previous and following metrics are stored in a dataframe object which is a matrix.)
a connectivity matrix enriched with cross talk information between qubits, expected execution time of each qubit, (Acampora, Pg. 3: “CXETi,j - an array of real values where each value represents the execution time (in nanoseconds) of a CNOT gate using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P.” Examiner notes, as the CNOT gate uses qubits, measuring the execution time of the CNOT gate is also measuring the execution time of each qubit.)
a connectivity matrix enriched with CNOT error rates between each pair of qubits, (Acampora, Pg. 3: CXERi,j - an array of real values where each value represents the error rate of a CNOT using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P.”)
a connectivity matrix enriched with CNOT execution times between each pair of qubits, (Acampora, Pg. 3: “CXETi,j - an array of real values where each value represents the execution time (in nanoseconds) of a CNOT gate using qi as control qubit and qj as target qubit for each (qi , qj ) ∈ P.”)
a transverse relaxation time, (Acampora, Pg. 4: “T2 – an array of real values where each value represents the transverse relaxation time in micro-seconds ( T2i ) characterizing a qubit qi of the processor P.”)
a longitudinal relaxation time, (Acampora, Pg. 4: “T1 - an array of real values where each value represents the longitudinal relaxation time ( T1i ) in micro-seconds characterizing a qubit qi of the processor P.”)
and a readout error are collected for each of the target quantum systems; (Acampora, Pg. 3: “ER0 an array of real values where each value ER0i represents the readout error characterizing a qubit qi of the processor P.”)
generating random quantum circuits (Acampora, Pg. 5: “Generation of a random quantum circuit using the generator provided by Qiskit: in detail, this step is useful to create random quantum circuits composed of single and multi-qubits logical gates.”)
and collecting transpilation metrics for the random quantum circuits for each of the selected target quantum systems; (Acampora, Pg. 6: “At this point, the total number of controlled not quantum gates Ncx is collected together with the matrix Ncx, whose item [i, j] contains the number of CX gates between the ith and the jth qubit of the circuit where the further is the control qubit and the latter the target one. Ncx and Ncxi,j are inserted in the dataframe”; “The calibration data contains all the features related to the processor P described in previous section, that are therefore collected and inserted in the pandas dataframe”; Examiner notes, “previous section” refers to the metrics previously cited and also on pg. 3-4. )
and including the collected features and the transpilation metrics in a training dataset. (Dukatz, Fig. 3 – 306; [0106]: “The system obtains a second set of data, the second set of data comprising data representing properties associated with using the one or more quantum computing resources to solve the multiple computational tasks (step 306).”; Examiner notes, Dukatz in view of Acampora would utilize Acampora’s metrics in training the model in Dukatz as previously combined, therefore would report the metrics to the machine learning module for training).
Dukatz in view of Acampora does not teach cross talk information between qubits.
However, in analogous art, Gunnels teaches cross talk information between qubits ([0081]: “Component 410 analyzes a set of quantum gate parameters...In an embodiment, calibration operation 412 returns a set of quantum gate parameters values for a plurality of quantum gates of the quantum processor 414. For example, gate error rates, gate speeds, gate cross talk matrix, and other quantum gate parameter values can be determined by the calibration operation”).
It would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to combine the reporting of cross talk error information from Gunnels with the systems and methods of Dukatz in view of Acampora, allowing the data containing properties associated with quantum computing resources in Dukatz in view of Acampora to also contain cross talk information. Therefore, along with the other metrics from Dukatz in view of Acampora, the cross talk information from Gunnels may also be used to train models when reported to the machine learning module and considered in the output from the machine learning module. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, to be able to track unwanted interactions between qubits on the quantum computing resources, thus being able to improve measuring how a task’s accuracy will be affected (see at least Gunnels, [0015]).
Response to Arguments
Applicant's arguments filed 09/09/2025 have been fully considered but they are not persuasive.
Applicant’s arguments are summarized as follows:
The Specification has been sufficiently amended to overcome its objection; it should be withdrawn.
Claims 3, 8, 13, and 18 have been sufficiently amended to overcome their rejections under 35 U.S.C. 112(b) as being indefinite; they should be withdrawn.
Claims 1-20 have been sufficiently amended to overcome their rejections under 35 U.S.C. 101; they should be withdrawn.
The rejection of Claims 1, 3, 10, 11, 13, and 20 under obviousness-type double patenting in view of claims 1, 4, 6, 11, 14, and 16 of copending Application 18/174,328 (‘328) is moot due to the amendments to the Claims. The Applicant requests for the rejection to be held in abeyance until a finding of otherwise allowable subject matter has been made.
Regarding the rejection of Claim 1 under 35 U.S.C. 103 as being unpatentable over Dukatz in view of Acampora, the newly amended aspects of the claim, where the transpilation metrics included in the output of a trained model include a transpilation time and an estimated circuit execution time, and that the transpilation metrics are generated for each quantum system available, meaning that different quantum workloads will be directed to different quantum systems, are not present in the cited references. Dukatz does not disclose transpilation metrics, and only mentions qubits and cost, with relation to availability, not transpilation. Acampara does not remedy these deficiencies or suggest transpilation metrics, as it discusses optimizing the time required to transpile a circuit using efficient mapping, which is distinct from predicting the time required to perform transpilation for all available quantum systems. A model to achieve the mapping in a shorter amount of time is distinct from Claim 1, which aims to estimate the time required to transpile a circuit. Therefore, Claims 1 and 11, and their dependent claims, are patentable over the cited references, and their rejections should be withdrawn.
Regarding A, the changes to the Specification have overcome the prior objection. Therefore, the objection is withdrawn.
Regarding B, the changes to Claims 3, 8, 13, and 18 have overcome the prior rejections under 35 U.S.C. 112(b). Therefore, the rejections are withdrawn.
Regarding C, the changes to Claims 1-20 have overcome the prior rejections under 35 U.S.C. 101, as they integrate the recited limitations relating to estimation of transpilation metrics into a practical application that selects a quantum system and subsequently executes the workload with the selected quantum system, presenting an improvement in the operation of the computing system due to improved execution time of workload processing. Therefore, the rejections are withdrawn.
Regarding D, since Claims 3 and 13 have been cancelled, and Claims 1 and 11 have been amended, the changes are sufficient to overcome the obviousness-type double patenting rejection with regards to Application ‘328. Therefore, the obviousness-type double patenting rejection is withdrawn.
Regarding E, the Examiner respectfully disagrees. Applicant argues that Acampara does not teach the amended limitations of Claim 1 regarding transpilation metrics being included in the output of a trained model include a transpilation time and an estimated circuit execution time, and that the transpilation metrics are generated for each quantum system available, meaning that different quantum workloads will be directed to different quantum systems, because it discusses optimizing the time required to transpile a circuit using efficient mapping, which is distinct from predicting the time required to perform transpilation for all available quantum systems. However, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. Therefore, Acampara, taken in combination with Dukatz as recited above, teaches all the limitations of the amended Claim 1 as further described above, as it in effect produces the same metrics. Therefore, the recited features can be found in the combination of references, amended Independent Claim 1 remains rejected under 35 U.S.C. 103 for the reasons stated above, and the combinations would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the application.
Contrary to Applicant’ arguments, because independent Claim 11 contains similar limitations to rejected Claim 1, and does not add limitations that overcome the rejection, it likewise remains rejected. Therefore, the recited features can be found in the combination of references, amended Independent Claim 11 remains rejected under 35 U.S.C. 103 for the reasons stated above, and the combinations would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the application.
Lastly, Independent claims 1 and 11 remain rejected for the reasons stated above. Thus, contrary to Applicant’s arguments, because the dependent Claims depend from unpatentable independent claims and do not add limitations that overcome the rejection, they likewise remain rejected.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20240104415 A1 Ramakrishnan et al. teaches training a quantum foundation model based on data representing operations of a quantum circuit and calibration data such as execution times and error rates. The Quantum foundation model can then be fined tuned to generate efficient circuits.
US 20230143072 A1 Flöther et al. teaches a system that selects a quantum circuit for mapping a set of classical features to a quantum feature space, provides the quantum circuit for execution by a quantum computer. Further comprises a training component that assesses quantum circuit selection based on output from execution of the quantum circuit by the quantum computer and causes the selection component to select a new quantum circuit based on the assessment.
US 20230110628 A1 Heckey et al. teaches a system and methods that guarantee compliance with service level agreements (maintaining a minimum quality of service) for quantum tasks submitted to quantum computing services.
US 20230315516 A1 Fitzpatrick et al. teaches methods for monitoring quantum jobs executing on quantum computers, recording state parameter values (for example, readout errors), and calibrating quantum computers based on the state parameter values, weighing a cost benefit reward when determining calibration strategy.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FARAZ T AKBARI/Examiner, Art Unit 2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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