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US Patent 10275717 Training quantum evolutions using sublogical controls

Patent 10275717 was granted and assigned to Google on April, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Google
Google
0
Current Assignee
Google
Google
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
102757170
Patent Inventor Names
Hartmut Neven0
Ryan Babbush0
Date of Patent
April 30, 2019
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Patent Application Number
151717780
Date Filed
June 2, 2016
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Patent Citations Received
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US Patent 11803611 Procedure to speed-up Variational Quantum Eigensolver calculations in quantum computers
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US Patent 11488049 Hybrid quantum-classical computer system and method for optimization
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US Patent 11507872 Hybrid quantum-classical computer system and method for performing function inversion
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US Patent 11537928 Quantum-classical system and method for matrix computations
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US Patent 11681774 Classically-boosted quantum optimization
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US Patent 11314536 Quantum variational method, apparatus, and storage medium for simulating quantum systems
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US Patent 11488050 Signal-to-noise based error detection
Patent Primary Examiner
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Whitney Moore
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Patent abstract

Methods, systems, and apparatus for training quantum evolutions using sub-logical controls. In one aspect, a method includes the actions of accessing quantum hardware, wherein the quantum hardware includes a quantum system comprising one or more multi-level quantum subsystems; one or more control devices that operate on the one or more multi-level quantum subsystems according to one or more respective control parameters that relate to a parameter of a physical environment in which the multi-level quantum subsystems are located; initializing the quantum system in an initial quantum state, wherein an initial set of control parameters form a parameterization that defines the initial quantum state; obtaining one or more quantum system observables and one or more target quantum states; and iteratively training until an occurrence of a completion event.

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