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公开(公告)号：US20170351974A1
公开(公告)日：2017-12-07
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
CPC classification number: G06N20/00 , G06K9/00986 , G06K9/6247 , G06K9/6249 , G06K9/6255 , G06N10/00
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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公开(公告)号：US20150006443A1
公开(公告)日：2015-01-01
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.
Abstract translation: 系统，方法和方面及其实施例涉及使用量子处理器学习的无监督或半监督特征。 为了实现无监督或半监督特征学习，量子处理器被编程为通过一个或多个数据集实现分层深度学习（称为HDL）。 系统和方法搜索，解析和检测一个或多个数据集或数据或数据集中的最大重复模式。 使用稀疏编码来检测数据中或跨数据的最大重复模式的实施例和方面。 稀疏编码的例子包括L0和L1稀疏编码。 一些实现可以涉及将标签附加，附加或附加到字典元素或一个或多个字典的组成元素。 标签和元素之间可能存在逻辑关联，标记为使得无监督或半监督特征学习的过程跨越了元素和合并，附加或附加的标签。
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公开(公告)号：US10467543B2
公开(公告)日：2019-11-05
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.
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Patent Agency Ranking
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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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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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.
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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.
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8.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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