Systems and methods for modeling noise sequences and calibrating quantum processors

    公开(公告)号:US12039465B2

    公开(公告)日:2024-07-16

    申请号:US16878364

    申请日:2020-05-19

    Inventor: Jack R. Raymond

    CPC classification number: G06N7/01 G01R29/26 G01R33/24 G06N10/00 B82Y35/00

    Abstract: Calibration techniques for devices of analog processors to remove time-dependent biases are described. Devices in an analog processor exhibit a noise spectrum that spans a wide range of frequencies, characterized by 1/f spectrum. Offset parameters are determined assuming only a given power spectral density. The algorithm determines a model for a measurable quantity of a device in an analog processor associated with a noise process and an offset parameter, determines the form of the spectral density of the noise process, approximates the noise spectrum by a discrete distribution via the digital processor, constructs a probability distribution of the noise process based on the discrete distribution and evaluates the probability distribution to determine optimized parameter settings to enhance computational efficiency.

    Systems and methods for heuristic algorithms with variable effort parameters

    公开(公告)号:US12254418B2

    公开(公告)日:2025-03-18

    申请号:US18126566

    申请日:2023-03-27

    Abstract: A heuristic solver is wrapped in a meta algorithm that will perform multiple sub-runs within the desired time limit, and expand or reduce the effort based on the time it has taken so far and the time left. The goal is to use the largest effort possible as this typically increases the probability of success. In another implementation, the meta algorithm iterates the time-like parameter from a small value, and determine the next test-value so as to minimize time to target collecting data at large effort only as necessary. The meta algorithm evaluates the energy of the solutions obtained to determine whether to increase or decrease the value of the time-like parameter. The heuristic algorithm may be Simulated Annealing, the heuristic algorithm may run on a quantum processor, including a quantum annealing processor or a gate-model quantum processor.

    SYSTEMS AND METHODS FOR HEURISTIC ALGORITHMS WITH VARIABLE EFFORT PARAMETERS

    公开(公告)号:US20230316094A1

    公开(公告)日:2023-10-05

    申请号:US18126566

    申请日:2023-03-27

    CPC classification number: G06N5/01 G06N10/40 G06F9/44505

    Abstract: A heuristic solver is wrapped in a meta algorithm that will perform multiple sub-runs within the desired time limit, and expand or reduce the effort based on the time it has taken so far and the time left. The goal is to use the largest effort possible as this typically increases the probability of success. In another implementation, the meta algorithm iterates the time-like parameter from a small value, and determine the next test-value so as to minimize time to target collecting data at large effort only as necessary. The meta algorithm evaluates the energy of the solutions obtained to determine whether to increase or decrease the value of the time-like parameter. The heuristic algorithm may be Simulated Annealing, the heuristic algorithm may run on a quantum processor, including a quantum annealing processor or a gate-model quantum processor.

    SYSTEMS AND METHODS FOR MODELING NOISE SEQUENCES AND CALIBRATING QUANTUM PROCESSORS

    公开(公告)号:US20200380396A1

    公开(公告)日:2020-12-03

    申请号:US16878364

    申请日:2020-05-19

    Inventor: Jack R. Raymond

    Abstract: Calibration techniques for devices of analog processors to remove time-dependent biases are described. Devices in an analog processor exhibit a noise spectrum that spans a wide range of frequencies, characterized by 1/f spectrum. Offset parameters are determined assuming only a given power spectral density. The algorithm determines a model for a measurable quantity of a device in an analog processor associated with a noise process and an offset parameter, determines the form of the spectral density of the noise process, approximates the noise spectrum by a discrete distribution via the digital processor, constructs a probability distribution of the noise process based on the discrete distribution and evaluates the probability distribution to determine optimized parameter settings to enhance computational efficiency.

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