-
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.
-
42.发明授权公开(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
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：US11501195B2
公开(公告)日：2022-11-15
申请号：US15641030
申请日：2017-07-03
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.
-
44.发明申请公开(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：US11461644B2
公开(公告)日：2022-10-04
申请号：US16682976
申请日：2019-11-13
Applicant: D-WAVE SYSTEMS INC.
Inventor： Arash Vahdat , Mostafa S. Ibrahim , William G. Macready
Abstract: Fully-supervised semantic segmentation machine learning models are augmented by ancillary machine learning models which generate high-detail predictions from low-detail, weakly-supervised data. The combined model can be trained over both fully- and weakly-supervised data. Only the primary model is required for inference, post-training. The combined model can be made self-correcting during training by adjusting the ancillary model's output based on parameters learned over both the fully- and weakly-supervised data. The self-correction module may combine the output of the primary and ancillary models in various ways, including through linear combinations and via neural networks. The self-correction module and ancillary model may benefit from disclosed pre-training techniques.
-
Patent Agency Ranking
公开(公告)号：US11410067B2
公开(公告)日：2022-08-09
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.
-
公开(公告)号：US11386346B2
公开(公告)日：2022-07-12
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.
-
48.发明申请公开(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
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
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.
-
公开(公告)号：US09727824B2
公开(公告)日：2017-08-08
申请号：US14316372
申请日：2014-06-26
Applicant: D-Wave Systems Inc.
Inventor： Geordie Rose , Suzanne Gildert , William G. Macready , Dominic Christoph Walliman
CPC classification number: G06N99/005 , G06K9/00986 , G06K9/6247 , G06K9/6249 , G06K9/6255 , 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.
-
-
-
-
-
-
-
-
-
Search Results
52
records
(took 0.039 seconds)
