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.

公开(公告)号：US11625612B2

公开(公告)日：2023-04-11

申请号：US16779035

申请日：2020-01-31

Applicant: D-WAVE SYSTEMS INC.

Inventor： Arash Vahdat ,  Mani Ranjbar ,  Mehran Khodabandeh ,  William G. Macready ,  Zhengbing Bian

IPC: G06N3/08 ,  G06N5/02 ,  G06N5/04 ,  G06N3/088 ,  G06N5/022 ,  G06N5/045

Abstract: The domain adaptation problem is addressed by using the predictions of a trained model over both source and target domain to retain the model with the assistance of an auxiliary model and a modified objective function. Inaccuracy in the model's predictions in the target domain is treated as noise and is reduced by using a robust learning framework during retraining, enabling unsupervised training in the target domain. Applications include object detection models, where noise in retraining is reduced by explicitly representing label noise and geometry noise in the objective function and using the ancillary model to inject information about label noise.

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

IPC: G06N99/00 ,  G06F15/18 ,  G06N3/04 ,  G06F15/80 ,  G06K9/00

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.

公开(公告)号：US20230385668A1

公开(公告)日：2023-11-30

申请号：US18203880

申请日：2023-05-31

Applicant: D-WAVE SYSTEMS INC.

Inventor： Murray C. Thom ,  Aidan P. Roy ,  Fabian A. Chudak ,  Zhengbing Bian ,  William G. Macready ,  Robert B. Israel ,  Kelly T. R. Boothby ,  Sheir Yarkoni ,  Yanbo Xue ,  Dmytro Korenkevych

IPC: G06N10/00

CPC classification number: G06N10/00

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples. A controller causes a processing operation on the partial samples to generate complete samples.

公开(公告)号：US10599988B2

公开(公告)日：2020-03-24

申请号：US15448361

申请日：2017-03-02

Applicant: D-Wave Systems Inc.

Inventor： Murray C. Thom ,  Aidan P. Roy ,  Fabian A. Chudak ,  Zhengbing Bian ,  William G. Macready ,  Robert B. Israel ,  Kelly T.R. Boothby ,  Sheir Yarkoni ,  Yanbo Xue ,  Dmytro Korenkevych

IPC: G06N10/00

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples. A controller causes a processing operation on the partial samples to generate complete samples.

公开(公告)号：US20170255629A1

公开(公告)日：2017-09-07

申请号：US15448361

申请日：2017-03-02

Applicant: D-Wave Systems Inc.

Inventor： Murray C. Thom ,  Aidan P. Roy ,  Fabian A. Chudak ,  Zhengbing Bian ,  William G. Macready ,  Robert B. Israel ,  Tomas J. Boothby ,  Sheir Yarkoni ,  Yanbo Xue ,  Dmytro Korenkevych

IPC: G06F17/30 ,  G06N99/00

CPC classification number: G06N10/00

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples. A controller causes a processing operation on the partial samples to generate complete samples.

公开(公告)号：US20150205759A1

公开(公告)日：2015-07-23

申请号：US14671862

申请日：2015-03-27

Applicant: D-WAVE SYSTEMS INC.

Inventor： Robert Israel ,  William G. Macready ,  Zhengbing Bian ,  Fabian Chudak ,  Mani Ranjbar

IPC: G06F17/11

CPC classification number: G06F17/11 ,  G06N99/002

Abstract: Methods and systems represent constraint as an Ising model penalty function and a penalty gap associated therewith, the penalty gap separating a set of feasible solutions to the constraint from a set of infeasible solutions to the constraint; and determines the Ising model penalty function subject to the bounds on the programmable parameters imposed by the hardware limitations of the second processor, where the penalty gap exceeds a predetermined threshold greater than zero. Such may be employed to find quantum binary optimization problems and associated gap values employing a variety of techniques.

Abstract translation: 方法和系统表示作为Ising模型惩罚函数的约束和与之相关联的惩罚差距，将一组可行解与约束的惩罚差距从一组不可行解解决定到约束; 并且确定Ising模型惩罚函数受到由第二处理器的硬件限制所强加的可编程参数的界限的影响，其中惩罚间隔超过大于零的预定阈值。 可以采用这种方法来找到采用各种技术的量子二进制优化问题和相关的间隙值。

公开(公告)号：US11704586B2

公开(公告)日：2023-07-18

申请号：US17739411

申请日：2022-05-09

Applicant: D-WAVE SYSTEMS INC.

Inventor： Murray C. Thom ,  Aidan P. Roy ,  Fabian A. Chudak ,  Zhengbing Bian ,  William G. Macready ,  Robert B. Israel ,  Kelly T. R. Boothby ,  Sheir Yarkoni ,  Yanbo Xue ,  Dmytro Korenkevych

IPC: G06N10/00

CPC classification number: G06N10/00

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples. A controller causes a processing operation on the partial samples to generate complete samples.

公开(公告)号：US11348026B2

公开(公告)日：2022-05-31

申请号：US16778295

申请日：2020-01-31

Applicant: D-WAVE SYSTEMS INC.

Inventor： Murray C. Thom ,  Aidan P. Roy ,  Fabian A. Chudak ,  Zhengbing Bian ,  William G. Macready ,  Robert B. Israel ,  Kelly T. R. Boothby ,  Sheir Yarkoni ,  Yanbo Xue ,  Dmytro Korenkevych

IPC: G06N10/00

Abstract: Computational systems implement problem solving using hybrid digital/quantum computing approaches. A problem may be represented as a problem graph which is larger and/or has higher connectivity than a working and/or hardware graph of a quantum processor. A quantum processor may be used determine approximate solutions, which solutions are provided as initial states to one or more digital processors which may implement classical post-processing to generate improved solutions. Techniques for solving problems on extended, more-connected, and/or “virtual full yield” variations of the processor's actual working and/or hardware graphs are provided. A method of operation in a computational system comprising a quantum processor includes partitioning a problem graph into sub-problem graphs, and embedding a sub-problem graph onto the working graph of the quantum processor. The quantum processor and a non-quantum processor-based device generate partial samples. A controller causes a processing operation on the partial samples to generate complete samples.

公开(公告)号：US20200257984A1

公开(公告)日：2020-08-13

申请号：US16779035

申请日：2020-01-31

Applicant: D-WAVE SYSTEMS INC.

Inventor： Arash Vahdat ,  Mani Ranjbar ,  Mehran Khodabandeh ,  William G. Macready ,  Zhengbing Bian

IPC: G06N3/08 ,  G06N5/04 ,  G06N5/02

Abstract: The domain adaptation problem is addressed by using the predictions of a trained model over both source and target domain to retain the model with the assistance of an auxiliary model and a modified objective function. Inaccuracy in the model's predictions in the target domain is treated as noise and is reduced by using a robust learning framework during retraining, enabling unsupervised training in the target domain. Applications include object detection models, where noise in retraining is reduced by explicitly representing label noise and geometry noise in the objective function and using the ancillary model to inject information about label noise.