REFINING QUBIT CALIBRATION MODELS USING SUPERVISED LEARNING

    公开(公告)号:US20210081816A1

    公开(公告)日:2021-03-18

    申请号:US16772387

    申请日:2017-12-15

    Applicant: Google LLC

    Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.

    CALIBRATION OF QUANTUM PROCESSOR OPERATOR PARAMETERS

    公开(公告)号:US20230325696A1

    公开(公告)日:2023-10-12

    申请号:US18311178

    申请日:2023-05-02

    Applicant: GOOGLE LLC

    Inventor: Paul Klimov

    CPC classification number: G06N10/00 G06F15/82 G06N10/60

    Abstract: Methods, systems and apparatus for determining operating parameters for a quantum processor including multiple interacting qubits. In one aspect, a method includes generating a graph of nodes and edges, wherein each node represents a respective qubit and is associated with an operating parameter of the respective qubit, and wherein each edge represents a respective interaction between two qubits and is associated with an operating parameter of the respective interaction; selecting an algorithm that traverses the graph based on a traversal rule; identifying one or multiple disjoint subsets of nodes or one or multiple disjoint subsets of edges, wherein nodes in a subset of nodes and edges in a subset of edges are related via the traversal rule; and determining calibrated values for the nodes or edges in each subset using a stepwise constrained optimization process where constraints are determined using previously calibrated operating parameters.

    Refining qubit calibration models using supervised learning

    公开(公告)号:US11556813B2

    公开(公告)日:2023-01-17

    申请号:US16772387

    申请日:2017-12-15

    Applicant: Google LLC

    Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.

    REFINING QUBIT CALIBRATION MODELS USING SUPERVISED LEARNING

    公开(公告)号:US20230306292A1

    公开(公告)日:2023-09-28

    申请号:US18088303

    申请日:2022-12-23

    Applicant: Google LLC

    CPC classification number: G06N5/04 G06N10/00 G06N20/00

    Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.

    OPTIMIZING QUBIT OPERATING FREQUENCIES

    公开(公告)号:US20220300847A1

    公开(公告)日:2022-09-22

    申请号:US17730963

    申请日:2022-04-27

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.

    Refining qubit calibration models using supervised learning

    公开(公告)号:US11829844B2

    公开(公告)日:2023-11-28

    申请号:US18088303

    申请日:2022-12-23

    Applicant: Google LLC

    CPC classification number: G06N10/00 G06N5/04 G06N20/00

    Abstract: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.

    Optimizing qubit operating frequencies

    公开(公告)号:US11361241B2

    公开(公告)日:2022-06-14

    申请号:US16971512

    申请日:2018-03-02

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.

    OPTIMIZING QUBIT OPERATING FREQUENCIES

    公开(公告)号:US20210334689A1

    公开(公告)日:2021-10-28

    申请号:US16971512

    申请日:2018-03-02

    Applicant: Google LLC

    Abstract: Methods, systems, and apparatus for determining frequencies at which to operate interacting qubits arranged as a two dimensional grid in a quantum device. In one aspect, a method includes the actions of defining a first cost function that characterizes technical operating characteristics of the system. The cost function maps qubit operation frequency values to a cost corresponding to an operating state of the quantum device; applying one or more constraints to the defined first cost function to define an adjusted cost function; and adjusting qubit operation frequency values to vary the cost according to the adjusted cost function such that the operating state of the quantum device is improved.

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