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    Systems and methods for quantum processing of data
    31.
    发明授权
    Systems and methods for quantum processing of data 有权

    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

    IPC: G06N99/00 ,  G06K9/62 ,  G06K9/00

    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.

    QUANTUM PROCESSOR BASED SYSTEMS AND METHODS THAT MINIMIZE AN OBJECTIVE FUNCTION
    33.
    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

    IPC: G06N99/00 ,  G06N7/00

    CPC classification number: G06N10/00 ,  B82Y10/00 ,  G06N3/00 ,  G06N3/12 ,  G06N5/02 ,  G06N7/005

    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。

    Systems and methods for solving computational problems
    35.
    发明授权
    Systems and methods for solving computational problems 有权
    用于解决计算问题的系统和方法

    公开(公告)号：US09026574B2

    公开(公告)日：2015-05-05

    申请号：US13678266

    申请日：2012-11-15

    Applicant: D-Wave Systems Inc.

    Inventor： William Macready ,  Geordie Rose ,  Thomas Mahon ,  Peter Love ,  Marshall Drew-Brook

    IPC: G06G7/32 ,  G06F17/00 ,  G06N5/00 ,  G06F17/10 ,  G06F17/11 ,  G06N99/00 ,  B82Y10/00

    CPC classification number: G06F17/10 ,  B82Y10/00 ,  G06F17/11 ,  G06N99/002

    Abstract: Solving computational problems may include generating a logic circuit representation of the computational problem, encoding the logic circuit representation as a discrete optimization problem, and solving the discrete optimization problem using a quantum processor. Output(s) of the logic circuit representation may be clamped such that the solving involves effectively executing the logic circuit representation in reverse to determine input(s) that corresponds to the clamped output(s). The representation may be of a multiplication circuit. The discrete optimization problem may be composed of a set of miniature optimization problems, where each miniature optimization problem encodes a respective logic gate from the logic circuit representation. A multiplication circuit may employ binary representations of factors, and these binary representations may be decomposed to reduce the total number of variables required to represent the multiplication circuit.

    Abstract translation: 解决计算问题可能包括生成计算问题的逻辑电路表示，将逻辑电路表示编码为离散优化问题，以及使用量子处理器来解决离散优化问题。 可以钳位逻辑电路表示的输出，使得解决方案涉及有效地执行逻辑电路表示，以确定对应于被钳位的输出的输入。 该表示可以是乘法电路。 离散优化问题可以由一组微型优化问题组成，其中每个微型优化问题从逻辑电路表示编码相应的逻辑门。 乘法电路可以采用因子的二进制表示，并且这些二进制表示可以被分解以减少表示乘法电路所需的变量的总数。

    ANALOG PROCESSOR COMPRISING QUANTUM DEVICES
    36.
    发明申请
    ANALOG PROCESSOR COMPRISING QUANTUM DEVICES 有权
    包含量子器件的模拟处理器

    公开(公告)号：US20140229705A1

    公开(公告)日：2014-08-14

    申请号：US14175731

    申请日：2014-02-07

    Applicant: D-Wave Systems Inc.

    Inventor： Alexander Maassen van den Brink ,  Peter Love ,  Mohammad H. S. Amin ,  Geordie Rose ,  David Grant ,  Miles F. H. Steininger ,  Paul I. Bunyk ,  Andrew J. Berkley

    IPC: G06F15/76

    CPC classification number: G06F15/80 ,  B82Y10/00 ,  G06F15/76 ,  G06N99/002 ,  H03K3/38 ,  H04L67/12

    Abstract: Analog processors for solving various computational problems are provided. Such analog processors comprise a plurality of quantum devices, arranged in a lattice, together with a plurality of coupling devices. The analog processors further comprise bias control systems each configured to apply a local effective bias on a corresponding quantum device. A set of coupling devices in the plurality of coupling devices is configured to couple nearest-neighbor quantum devices in the lattice. Another set of coupling devices is configured to couple next-nearest neighbor quantum devices. The analog processors further comprise a plurality of coupling control systems each configured to tune the coupling value of a corresponding coupling device in the plurality of coupling devices to a coupling. Such quantum processors further comprise a set of readout devices each configured to measure the information from a corresponding quantum device in the plurality of quantum devices.

    Abstract translation: 提供了用于解决各种计算问题的模拟处理器。 这种模拟处理器包括与多个耦合装置一起布置成格子的多个量子器件。 模拟处理器进一步包括偏置控制系统，每个偏置控制系统被配置为在对应的量子器件上施加局部有效偏置。 多个耦合装置中的一组耦合装置被配置为耦合格子中的最近邻量子器件。 另一组耦合器件配置成耦合下一个最近邻量子器件。 模拟处理器还包括多个耦合控制系统，每个耦合控制系统被配置为将多个耦合装置中的对应耦合装置的耦合值调谐到耦合。 这种量子处理器还包括一组读出装置，每个读出装置被配置为从多个量子器件中的对应的量子器件测量信息。

    Systems and methods for quantum processing of data
    38.
    发明授权
    Systems and methods for quantum processing of data 有权

    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

    IPC: G06N99/00 ,  G06N20/00 ,  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.

    SYSTEMS AND METHODS FOR QUANTUM PROCESSING OF DATA
    40.
    发明申请
    SYSTEMS AND METHODS FOR QUANTUM PROCESSING OF DATA 审中-公开

    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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