Systems and methods for quantum processing of data

    公开(公告)号:US10318881B2

    公开(公告)日:2019-06-11

    申请号:US14316366

    申请日:2014-06-26

    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

    公开(公告)号:US20160321559A1

    公开(公告)日:2016-11-03

    申请号:US14316366

    申请日:2014-06-26

    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.

    SYSTEMS AND METHODS FOR QUANTUM PROCESSING OF DATA

    公开(公告)号:US20170351974A1

    公开(公告)日:2017-12-07

    申请号:US15641030

    申请日:2017-07-03

    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
    4.
    发明申请
    SYSTEMS AND METHODS FOR QUANTUM PROCESSING OF DATA 有权
    数据处理数据的系统和方法

    公开(公告)号:US20150006443A1

    公开(公告)日:2015-01-01

    申请号:US14316372

    申请日:2014-06-26

    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。

    Systems and methods for quantum processing of data

    公开(公告)号:US09727824B2

    公开(公告)日:2017-08-08

    申请号:US14316372

    申请日:2014-06-26

    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.

Patent Agency Ranking