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1.发明公开公开(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.
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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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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.
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公开(公告)号：US20200019879A1
公开(公告)日：2020-01-16
申请号：US16357773
申请日：2019-03-19
Applicant: D-WAVE SYSTEMS INC.
Inventor： Yanbo Xue , William G. Macready
Abstract: Techniques are provided for computing problems represented as directed graphical models via quantum processors with topologies and coupling physics which correspond to undirected graphs. These include techniques for generating approximations of Bayesian networks via a quantum processor capable of computing problems based on a Markov network-based representation of such problems. Approximations may be generated by moralization of Bayesian networks to Markov networks, learning of Bayesian networks' probability distributions by Markov networks' probability distributions, or otherwise, and are trained by executing the resulting Markov network on the quantum processor.
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公开(公告)号：US10318881B2
公开(公告)日：2019-06-11
申请号：US14316366
申请日：2014-06-26
Applicant: D-Wave Systems Inc.
Inventor： Geordie Rose , Suzanne Gildert , William G. Macready , Dominic Christoph Walliman
Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.
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Patent Agency Ranking
公开(公告)号：US20170255871A1
公开(公告)日：2017-09-07
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
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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7.发明申请公开(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
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.
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公开(公告)号：US20160321559A1
公开(公告)日：2016-11-03
申请号：US14316366
申请日：2014-06-26
Applicant: D-Wave Systems Inc.
Inventor： Geordie Rose , Suzanne Gildert , William G. Macready , Dominic Christoph Walliman
IPC: G06N99/00
CPC classification number: G06N99/005 , G06N99/002
Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.
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9.发明申请公开(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。
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公开(公告)号：US12198051B2
公开(公告)日：2025-01-14
申请号：US18096198
申请日：2023-01-12
Applicant: D-WAVE SYSTEMS INC.
Inventor： William G. Macready , Jason T. Rolfe
IPC: G06N3/047 , G06F18/214 , G06N3/045 , G06N3/08 , G06N10/00
Abstract: Collaborative filtering systems based on variational autoencoders (VAEs) are provided. VAEs may be trained on row-wise data without necessarily training a paired VAE on column-wise data (or vice-versa), and may optionally be trained via minibatches. The row-wise VAE models the output of the corresponding column-based VAE as a set of parameters and uses these parameters in decoding. In some implementations, a paired VAE is provided which receives column-wise data and models row-wise parameters; each of the paired VAEs may bind their learned column- or row-wise parameters to the output of the corresponding VAE. The paired VAEs may optionally be trained via minibatches. Unobserved data may be explicitly modelled. Methods for performing inference with such VAE-based collaborative filtering systems are also disclosed, as are example applications to search and anomaly detection.
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