公开(公告)号：US20200167685A1

公开(公告)日：2020-05-28

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：US12039407B2

公开(公告)日：2024-07-16

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: G06N10/20 ,  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：US20220335320A1

公开(公告)日：2022-10-20

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

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：US20210365826A1

公开(公告)日：2021-11-25

申请号：US17323143

申请日：2021-05-18

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

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：US10275422B2

公开(公告)日：2019-04-30

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 ,  G06N99/00

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.