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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：US20220092152A1
公开(公告)日：2022-03-24
申请号：US17533384
申请日：2021-11-23
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 may be 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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3.发明申请公开(公告)号：US20180247200A1
公开(公告)日：2018-08-30
申请号：US15753666
申请日：2016-08-18
Applicant: D-Wave Systems Inc.
Inventor： Jason Rolfe
CPC classification number: G06N3/086 , G06N3/0445 , G06N3/0454 , G06N3/0472 , G06N3/08 , G06N3/084 , G06N3/088 , G06N10/00
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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior.
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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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5.发明申请公开(公告)号：US20180101784A1
公开(公告)日：2018-04-12
申请号：US15725600
申请日：2017-10-05
Applicant: D-Wave Systems Inc.
Inventor： Jason Rolfe , William G. Macready , Zhengbing Bian , Fabian A. Chudak
CPC classification number: G06N10/00 , G06F15/80 , G06K9/00986 , G06K9/6256 , G06N3/0454 , G06N3/0472 , G06N3/084 , G06N3/088 , G06N7/005 , G06N20/00
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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.
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Patent Agency Ranking
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：US20220076131A1
公开(公告)日：2022-03-10
申请号：US17481568
申请日：2021-09-22
Applicant: D-WAVE SYSTEMS INC.
Inventor： Jason Rolfe
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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior.
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公开(公告)号：US11042811B2
公开(公告)日：2021-06-22
申请号：US15725600
申请日：2017-10-05
Applicant: D-Wave Systems Inc.
Inventor： Jason Rolfe , William G. Macready , Zhengbing Bian , Fabian A. Chudak
IPC: G06N10/00 , G06N3/04 , G06K9/00 , G06N3/08 , G06F15/80 , G06N20/00 , G06K9/62 , G06N20/10 , G06N7/00
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 computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.
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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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公开(公告)号：US20180308007A1
公开(公告)日：2018-10-25
申请号：US15766755
申请日：2016-10-14
Applicant: D-WAVE SYSTEMS INC.
Inventor： Mohammad H.S. Amin , Evgeny Andriyash , Jason Rolfe
IPC: G06N99/00
CPC classification number: G06N99/005 , B82Y10/00 , G06N3/0445 , G06N99/002
Abstract: A hybrid computer generates samples for machine learning. The hybrid computer includes a processor that implements a Boltzmann machine, e.g., a quantum Boltzmann machine, which returns equilibrium samples from eigenstates of a quantum Hamiltonian. Subsets of samples are provided to training and validations modules. Operation can include: receiving a training set; preparing a model described by an Ising Hamiltonian; initializing model parameters; segmenting the training set into subsets; creating a sample set by repeatedly drawing samples until the determined number of samples has been drawn; and updating the model. Operation can include partitioning the training set into input and output data sets, and determining a conditional probability distribution that describes a probability of observing an output vector given a selected input vector, e.g., determining a conditional probability by performing a number of operations to minimize an upper bound for a log-likelihood of the conditional probability distribution.
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