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：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.

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：US11468293B2

公开(公告)日：2022-10-11

申请号：US16714103

申请日：2019-12-13

Applicant: D-WAVE SYSTEMS INC.

Inventor： Fabian A. Chudak

IPC: G06N3/04 ,  G06N10/00 ,  G06N3/06 ,  G06N3/08 ,  G06F17/11

Abstract: A hybrid computing system comprising a quantum computer and a digital computer employs a digital computer to use machine learning methods for post-processing samples drawn from the quantum computer. Post-processing samples can include simulating samples drawn from the quantum computer. Machine learning methods such as generative adversarial networks (GANs) and conditional GANs are applied. Samples drawn from the quantum computer can be a target distribution. A generator of a GAN generates samples based on a noise prior distribution and a discriminator of a GAN measures the distance between the target distribution and a generative distribution. A generator parameter and a discriminator parameter are respectively minimized and maximized.

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.