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公开(公告)号:US20170255871A1
公开(公告)日:2017-09-07
申请号:US15452438
申请日:2017-03-07
Applicant: D-Wave Systems Inc.
Inventor: William G. Macready , Firas Hamze , Fabian A. Chudak , Mani Ranjbar , Jack R. Raymond , Jason T. Rolfe
Abstract: A hybrid computer comprising a quantum processor can be operated to perform a scalable comparison of high-entropy samplers. Performing a scalable comparison of high-entropy samplers can include comparing entropy and KL divergence of post-processed samplers. A hybrid computer comprising a quantum processor generates samples for machine learning. The quantum processor is trained by matching data statistics to statistics of the quantum processor. The quantum processor is tuned to match moments of the data.
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公开(公告)号:US12039465B2
公开(公告)日:2024-07-16
申请号:US16878364
申请日:2020-05-19
Applicant: D-WAVE SYSTEMS INC.
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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公开(公告)号:US10817796B2
公开(公告)日:2020-10-27
申请号:US15452438
申请日:2017-03-07
Applicant: D-Wave Systems Inc.
Inventor: William G. Macready , Firas Hamze , Fabian A. Chudak , Mani Ranjbar , Jack R. Raymond , Jason T. Rolfe
IPC: G06N10/00 , G06N7/00 , G06N20/00 , G06K9/62 , G06F111/10
Abstract: A hybrid computer comprising a quantum processor can be operated to perform a scalable comparison of high-entropy samplers. Performing a scalable comparison of high-entropy samplers can include comparing entropy and KL divergence of post-processed samplers. A hybrid computer comprising a quantum processor generates samples for machine learning. The quantum processor is trained by matching data statistics to statistics of the quantum processor. The quantum processor is tuned to match moments of the data.
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公开(公告)号:US12254418B2
公开(公告)日:2025-03-18
申请号:US18126566
申请日:2023-03-27
Applicant: D-WAVE SYSTEMS INC.
Inventor: Pau Farré Pérez , Jack R. Raymond
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.
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公开(公告)号:US20210019647A1
公开(公告)日:2021-01-21
申请号:US17030576
申请日:2020-09-24
Applicant: D-WAVE SYSTEMS INC.
Inventor: William G. Macready , Firas Hamze , Fabian A. Chudak , Mani Ranjbar , Jack R. Raymond , Jason T. Rolfe
Abstract: A hybrid computer comprising a quantum processor can be operated to perform a scalable comparison of high-entropy samplers. Performing a scalable comparison of high-entropy samplers can include comparing entropy and KL divergence of post-processed samplers. A hybrid computer comprising a quantum processor generates samples for machine learning. The quantum processor is trained by matching data statistics to statistics of the quantum processor. The quantum processor is tuned to match moments of the data.
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公开(公告)号:US12229632B2
公开(公告)日:2025-02-18
申请号:US17030576
申请日:2020-09-24
Applicant: D-WAVE SYSTEMS INC.
Inventor: William G. Macready , Firas Hamze , Fabian A. Chudak , Mani Ranjbar , Jack R. Raymond , Jason T. Rolfe
IPC: G06N10/00 , G06F18/2415 , G06F111/10 , G06N7/01 , G06N20/00
Abstract: A hybrid computer comprising a quantum processor can be operated to perform a scalable comparison of high-entropy samplers. Performing a scalable comparison of high-entropy samplers can include comparing entropy and KL divergence of post-processed samplers. A hybrid computer comprising a quantum processor generates samples for machine learning. The quantum processor is trained by matching data statistics to statistics of the quantum processor. The quantum processor is tuned to match moments of the data.
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公开(公告)号:US11410067B2
公开(公告)日:2022-08-09
申请号:US15753661
申请日:2016-08-18
Applicant: D-WAVE SYSTEMS INC.
Inventor: Jason Rolfe , Dmytro Korenkevych , Mani Ranjbar , Jack R. Raymond , William G. Macready
Abstract: A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the digital processor can operate as a restricted Boltzmann machine. The computational system can operate as a quantum-based deep belief network operating on a training data-set.
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公开(公告)号:US20230316094A1
公开(公告)日:2023-10-05
申请号:US18126566
申请日:2023-03-27
Applicant: D-WAVE SYSTEMS INC.
Inventor: Pau Farré Pérez , Jack R. Raymond
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.
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公开(公告)号:US20200380396A1
公开(公告)日:2020-12-03
申请号:US16878364
申请日:2020-05-19
Applicant: D-WAVE SYSTEMS INC.
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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公开(公告)号:US20200210876A1
公开(公告)日:2020-07-02
申请号:US15753661
申请日:2016-08-18
Applicant: D-Wave Systems Inc.
Inventor: Jason Rolfe , Dmytro Korenkevych , Mani Ranjbar , Jack R. Raymond , William G. Macready
Abstract: A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the digital processor can operate as a restricted Boltzmann machine. The computational system can operate as a quantum-based deep belief network operating on a training data-set.
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