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公开(公告)号：US09881256B2
公开(公告)日：2018-01-30
申请号：US15505522
申请日：2015-08-21
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , Andrew Douglas King , Jack Raymond , Aidan Patrick Roy , Robert Israel , Evgeny Andriyash , Catherine McGeoch , Mani Ranjbar
CPC classification number: G06N99/002 , G06F9/02 , G06F9/32 , G06F15/18 , G06F15/76 , G06F17/10 , G06N3/12
Abstract: Computational systems implement problem solving using heuristic solvers or optimizers. Such may iteratively evaluate a result of processing, and modify the problem or representation thereof before repeating processing on the modified problem, until a termination condition is reached. Heuristic solvers or optimizers may execute on one or more digital processors and/or one or more quantum processors. The system may autonomously select between types of hardware devices and/or types of heuristic optimization algorithms. Such may coordinate or at least partially overlap post-processing operations with processing operations, for instance performing post-processing on an ith batch of samples while generating an (i+1)th batch of samples, e.g., so post-processing operation on the ith batch of samples does not extend in time beyond the generation of the (i+1)th batch of samples. Heuristic optimizers selection is based on pre-processing assessment of the problem, e.g., based on features extracted from the problem and for instance, on predicted success.
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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：US10467543B2
公开(公告)日：2019-11-05
申请号：US14920235
申请日：2015-10-22
Applicant: D-Wave Systems Inc.
Inventor： William G. Macready , Mani Ranjbar , Firas Hamze , Geordie Rose , Suzanne Gildert
Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.
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公开(公告)号：US09588940B2
公开(公告)日：2017-03-07
申请号：US14676605
申请日：2015-04-01
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , James King , Evgeny Andriyash , Catherine McGeoch , Jack Raymond , Jason Rolfe , William G. Macready , Aaron Lott , Murray C. Thom
CPC classification number: G06F17/18 , G06N99/002 , G06N99/005
Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.
Abstract translation: 系统，设备，物品和方法通常涉及从可用概率分布中的采样。 样本可以用于创建期望的概率分布，例如用于计算技术中使用的计算值，包括：重要性采样和马尔可夫链蒙特卡洛系统。 模拟处理器可以作为采样发生器操作，例如通过以下方式来对模拟处理器进行编程：模拟处理器的可编程参数数量的配置，其对应于模拟处理器的量子位上的概率分布，演进模拟处理器， 并读出量子位的状态。 多个量子位中的量子位的状态对应于来自概率分布的样本。 采样装置的操作可以被概括为包括更新一组样本以包括来自概率分布的样本，并返回该组样本。
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公开(公告)号：US11238131B2
公开(公告)日：2022-02-01
申请号：US15399461
申请日：2017-01-05
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , James King , Evgeny Andriyash , Catherine McGeoch , Jack Raymond , Jason Rolfe , William G. Macready , Aaron Lott , Murray C. Thom
Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.
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Patent Agency Ranking
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：US20170255872A1
公开(公告)日：2017-09-07
申请号：US15505522
申请日：2015-08-21
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , Andrew Douglas King , Jack Raymond , Aidan Patrick Roy , Robert Israel , Evgeny Andriyash , Catherine McGeoch , Mani Ranjbar
CPC classification number: G06N99/002 , G06F9/02 , G06F9/32 , G06F15/18 , G06F15/76 , G06F17/10 , G06N3/12
Abstract: Computational systems implement problem solving using heuristic solvers or optimizers. Such may iteratively evaluate a result of processing, and modify the problem or representation thereof before repeating processing on the modified problem, until a termination condition is reached. Heuristic solvers or optimizers may execute on one or more digital processors and/or one or more quantum processors. The system may autonomously select between types of hardware devices and/or types of heuristic optimization algorithms. Such may coordinate or at least partially overlap post-processing operations with processing operations, for instance performing post-processing on an ith batch of samples while generating an (i+1)th batch of samples, e.g., so post-processing operation on the ith batch of samples does not extend in time beyond the generation of the (i+1)th batch of samples. Heuristic optimizers selection is based on pre-processing assessment of the problem, e.g., based on features extracted from the problem and for instance, on predicted success.
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公开(公告)号：US20150269124A1
公开(公告)日：2015-09-24
申请号：US14676605
申请日：2015-04-01
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , James King , Evgeny Andriyash , Catherine McGeoch , Jack Raymond , Jason Rolfe , William G. Macready , Aaron Lott , Murray C. Thom
IPC: G06F17/18
CPC classification number: G06F17/18 , G06N99/002 , G06N99/005
Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.
Abstract translation: 系统，设备，物品和方法通常涉及从可用概率分布中的采样。 样本可以用于创建期望的概率分布，例如用于计算技术中使用的计算值，包括：重要性采样和马尔可夫链蒙特卡洛系统。 模拟处理器可以作为采样发生器操作，例如通过以下方式来对模拟处理器进行编程：模拟处理器的可编程参数数量的配置，其对应于模拟处理器的量子位上的概率分布，演进模拟处理器， 并读出量子位的状态。 多个量子位中的量子位的状态对应于来自概率分布的样本。 采样装置的操作可以被概括为包括更新一组样本以包括来自概率分布的样本，并返回该组样本。
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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：US20170116159A1
公开(公告)日：2017-04-27
申请号：US15399461
申请日：2017-01-05
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , James King , Evgeny Andriyash , Catherine McGeoch , Jack Raymond , Jason Rolfe , William G. Macready , Aaron Lott , Murray C. Thom
Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.
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