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US Patent 9727824 Systems and methods for quantum processing of data

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
D-Wave Systems
D-Wave Systems
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
97278241
Patent Inventor Names
Geordie Rose1
Dominic Christoph Walliman1
Suzanne Gildert1
William G. Macready1
Date of Patent
August 8, 2017
1
Patent Application Number
143163721
Date Filed
June 26, 2014
1
Patent Citations Received
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2
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US Patent 11704586 Systems and methods for analog processing of problem graphs having arbitrary size and/or connectivity
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US Patent 11710038 Systems and methods for active learning from sparse training data
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US Patent 11861455 Systems and methods for performing quantum evolution in quantum computation
5
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US Patent 11941486 Quantum annealing debugging systems and methods
6
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US Patent 11947506 Method and system for mapping a dataset from a Hilbert space of a given dimension to a Hilbert space of a different dimension
7
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US Patent 11954562 Modular control in a quantum computing system
8
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US Patent 11989256 Method and system for solving the Lagrangian dual of a constrained binary quadratic programming problem using a quantum annealer
9
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Patent Primary Examiner
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Paulinho E Smith
1
Patent 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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