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公开(公告)号：US20200210876A1
公开(公告)日：2020-07-02
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.
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公开(公告)号：US09881256B2
公开(公告)日：2018-01-30
申请号：US15505522
申请日：2015-08-21
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , Andrew Douglas King , Jack Raymond , Aidan Patrick Roy , Robert Israel , Evgeny Andriyash , Catherine McGeoch , Mani Ranjbar
CPC classification number: G06N99/002 , G06F9/02 , G06F9/32 , G06F15/18 , G06F15/76 , G06F17/10 , G06N3/12
Abstract: Computational systems implement problem solving using heuristic solvers or optimizers. Such may iteratively evaluate a result of processing, and modify the problem or representation thereof before repeating processing on the modified problem, until a termination condition is reached. Heuristic solvers or optimizers may execute on one or more digital processors and/or one or more quantum processors. The system may autonomously select between types of hardware devices and/or types of heuristic optimization algorithms. Such may coordinate or at least partially overlap post-processing operations with processing operations, for instance performing post-processing on an ith batch of samples while generating an (i+1)th batch of samples, e.g., so post-processing operation on the ith batch of samples does not extend in time beyond the generation of the (i+1)th batch of samples. Heuristic optimizers selection is based on pre-processing assessment of the problem, e.g., based on features extracted from the problem and for instance, on predicted success.
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3.发明授权Quantum processor based systems and methods that minimize a continuous variable objective function 有权基于量子处理器的系统和方法使连续变量目标函数最小化公开(公告)号：US09424526B2
公开(公告)日：2016-08-23
申请号：US14280204
申请日：2014-05-16
Applicant: D-Wave Systems Inc.
Inventor： Mani Ranjbar
CPC classification number: G06N99/002 , B82Y10/00 , G06F17/11
Abstract: Computational techniques for mapping a continuous variable objective function into a discrete variable objective function problem that facilitate determining a solution of the problem via a quantum processor are described. The modified objective function is solved by minimizing the cost of the mapping via an iterative search algorithm.
Abstract translation: 描述了将连续变量目标函数映射到便于通过量子处理器确定问题的解的离散变量目标函数问题的计算技术。 通过迭代搜索算法最小化映射的成本来解决修改后的目标函数。
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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.
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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.
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Patent Agency Ranking
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.
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7.发明申请QUANTUM PROCESSOR BASED SYSTEMS AND METHODS THAT MINIMIZE AN OBJECTIVE FUNCTION 审中-公开基于量子处理器的系统和最小化目标函数的方法公开(公告)号：US20160042294A1
公开(公告)日：2016-02-11
申请号：US14920235
申请日：2015-10-22
Applicant: D-Wave Systems Inc.
Inventor： William G. Macready , Mani Ranjbar , Firas Hamze , Geordie Rose , Suzanne Gildert
Abstract: Quantum processor based techniques minimize an objective function for example by operating the quantum processor as a sample generator providing low-energy samples from a probability distribution with high probability. The probability distribution is shaped to assign relative probabilities to samples based on their corresponding objective function values until the samples converge on a minimum for the objective function. Problems having a number of variables and/or a connectivity between variables that does not match that of the quantum processor may be solved. Interaction with the quantum processor may be via a digital computer. The digital computer stores a hierarchical stack of software modules to facilitate interacting with the quantum processor via various levels of programming environment, from a machine language level up to an end-use applications level.
Abstract translation: 基于量子处理器的技术例如通过将量子处理器作为从具有高概率的概率分布提供低能量样本的样本发生器来操作来最小化目标函数。 概率分布的形状是根据相应的目标函数值为样本分配相对概率，直到样本收敛到目标函数的最小值为止。 可以解决具有与量子处理器不匹配的变量之间的多个变量和/或连接性的问题。 与量子处理器的交互可以通过数字计算机。 数字计算机存储分层堆栈的软件模块，以便于通过各种级别的编程环境从机器语言级别到最终使用应用级别与量子处理器进行交互。
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公开(公告)号：US20210289020A1
公开(公告)日：2021-09-16
申请号：US16336625
申请日：2017-09-26
Applicant: D-WAVE SYSTEMS INC.
Inventor： Jason T. Rolfe , William G. Macready , Mani Ranjbar , Mayssam Mohammad Nevisi
IPC: H04L29/08 , G06F15/173 , G06N7/08 , G06N10/00
Abstract: A digital processor runs a machine learning algorithm in parallel with a sampling server. The sampling sever may continuously or intermittently draw samples for the machine learning algorithm during execution of the machine learning algorithm, for example on a given problem. The sampling server may run in parallel (e.g., concurrently, overlapping, simultaneously) with a quantum processor to draw samples from the quantum processor.
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公开(公告)号：US20210019647A1
公开(公告)日：2021-01-21
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
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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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
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.
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