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    TRAINING QUANTUM EVOLUTIONS USING SUBLOGICAL CONTROLS
    31.
    发明申请
    TRAINING QUANTUM EVOLUTIONS USING SUBLOGICAL CONTROLS 有权

    公开(公告)号：US20210295198A1

    公开(公告)日：2021-09-23

    申请号：US17339125

    申请日：2021-06-04

    Applicant: Google LLC

    Inventor： Ryan Babbush ,  Hartmut Neven

    IPC: G06N10/00 ,  G06F15/16 ,  G06N20/00 ,  B82Y10/00

    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.

    QUANTUM HARDWARE CHARACTERIZED BY PROGRAMMABLE BOSE-HUBBARD HAMILTONIANS
    33.
    发明申请
    QUANTUM HARDWARE CHARACTERIZED BY PROGRAMMABLE BOSE-HUBBARD HAMILTONIANS 审中-公开

    公开(公告)号：US20200161530A1

    公开(公告)日：2020-05-21

    申请号：US16752404

    申请日：2020-01-24

    Applicant: Google LLC

    Inventor： Masoud Mohseni ,  Hartmut Neven

    IPC: H01L39/02 ,  G06N10/00

    Abstract: An apparatus includes a first group of superconducting cavities and a second group of superconducting cavities, each of which is configured to receive multiple photons. The apparatus includes couplers, where each coupler couples one superconducting cavity from the first group with one cavity from the second group such that the photons in the coupled superconducting cavities interact. A first superconducting cavity of the first group is connected to a second superconducting cavity of the second group, such that photons of the first and second superconducting cavities are shared by each of the first and second superconducting cavities. The first superconducting cavity is coupled to at least one other superconducting cavity of the first group to which the second superconducting cavities are coupled, and the second superconducting cavity is coupled to at least one other superconducting cavity of the second group to which the first superconducting cavities are coupled.

    QUANTUM STATISTIC MACHINE
    34.
    发明申请
    QUANTUM STATISTIC MACHINE 审中-公开

    公开(公告)号：US20190005402A1

    公开(公告)日：2019-01-03

    申请号：US16067560

    申请日：2016-12-22

    Applicant: Google LLC

    Inventor： Masoud Mohseni ,  Hartmut Neven

    IPC: G06N99/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing and programming quantum hardware for machine learning processes. A Quantum Statistic Machine (QSM) is described, consisting of three distinct classes of strongly interacting degrees of freedom including visible, hidden and control quantum subspaces or subsystems. The QSM is defined with a programmable non-equilibrium ergodic open quantum Markov chain with a unique attracting steady state in the space of density operators. The solution of an information processing task, such as a statistical inference or optimization task, can be encoded into the quantum statistics of an attracting steady state, where quantum inference is performed by minimizing the energy of a real or fictitious quantum Hamiltonian. The couplings of the QSM between the visible and hidden nodes may be trained to solve hard optimization or inference tasks.

    Quantum hardware characterized by programmable bose-hubbard Hamiltonians
    37.
    发明授权
    Quantum hardware characterized by programmable bose-hubbard Hamiltonians 有权

    公开(公告)号：US12089509B2

    公开(公告)日：2024-09-10

    申请号：US16752404

    申请日：2020-01-24

    Applicant: Google LLC

    Inventor： Masoud Mohseni ,  Hartmut Neven

    IPC: H01L39/02 ,  G06N10/00 ,  H10N60/80 ,  G06N7/01

    CPC classification number: H10N60/805 ,  G06N10/00 ,  G06N7/01

    Abstract: An apparatus includes a first group of superconducting cavities and a second group of superconducting cavities, each of which is configured to receive multiple photons. The apparatus includes couplers, where each coupler couples one superconducting cavity from the first group with one cavity from the second group such that the photons in the coupled superconducting cavities interact. A first superconducting cavity of the first group is connected to a second superconducting cavity of the second group, such that photons of the first and second superconducting cavities are shared by each of the first and second superconducting cavities. The first superconducting cavity is coupled to at least one other superconducting cavity of the first group to which the second superconducting cavities are coupled, and the second superconducting cavity is coupled to at least one other superconducting cavity of the second group to which the first superconducting cavities are coupled.

    Totally corrective boosting with cardinality penalization
    39.
    发明授权
    Totally corrective boosting with cardinality penalization 有权

    公开(公告)号：US11620573B1

    公开(公告)日：2023-04-04

    申请号：US16555943

    申请日：2019-08-29

    Applicant: Google LLC

    Inventor： Vasil S. Denchev ,  Hartmut Neven

    IPC: G06N20/00 ,  G06N10/00

    Abstract: Methods, systems, and apparatus, for totally corrective boosting with cardinality penalization are described. One of the methods includes obtaining initialization data identifying training examples, a dictionary of weak classifiers, and an active weak classifier matrix. Iterations of a totally corrective boosting with cardinality penalization process are performed, wherein each iteration performs operations comprising selecting a weak classifier from the dictionary of weak classifiers that most violates a constraint of a dual of the primal problem. The selected weak classifier is included in the active weak classifier matrix. The primal problem is optimized, and a discrete weight vector is determined. Weak classifiers are identified from the active weak classifier matrix with respective discrete weights greater than a threshold. The regularized risk is optimized, and a continuous weight vector is determined. The classifier is determined as an ensemble identified by the weak classifiers and the continuous weight vector.

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