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    TRAINING QUANTUM EVOLUTIONS USING SUBLOGICAL CONTROLS
    51.
    发明申请
    TRAINING QUANTUM EVOLUTIONS USING SUBLOGICAL CONTROLS 审中-公开

    公开(公告)号：US20190213495A1

    公开(公告)日：2019-07-11

    申请号：US16355293

    申请日：2019-03-15

    Applicant: Google LLC

    Inventor： Ryan Babbush ,  Hartmut Neven

    IPC: G06N10/00 ,  G06N20/00

    CPC classification number: G06N10/00 ,  B82Y10/00 ,  G06N20/00

    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 assisted optimization
    53.
    发明授权
    Quantum assisted optimization 有权

    公开(公告)号：US12260341B2

    公开(公告)日：2025-03-25

    申请号：US17933339

    申请日：2022-09-19

    Applicant: Google LLC

    Inventor： Vasil S. Denchev ,  Masoud Mohseni ,  Hartmut Neven

    IPC: G06N3/12 ,  G06N3/126 ,  G06N5/01 ,  G06N10/60

    Abstract: Methods and apparatus for quantum assisted optimization. In one aspect, a method includes obtaining a set of initial input states, applying one or more of (i) dynamical thermal fluctuations and (ii) cluster update algorithms to the set of input states and subsequent input states when the states evolve within the classical information processors, applying dynamical quantum fluctuations to the set of input states and subsequent states when the states evolve within the quantum systems and repeating the application steps until a desirable output state is obtained.

    UNIVERSAL CONTROL FOR IMPLEMENTING QUANTUM GATES
    54.
    发明申请
    UNIVERSAL CONTROL FOR IMPLEMENTING QUANTUM GATES 有权

    公开(公告)号：US20250045613A1

    公开(公告)日：2025-02-06

    申请号：US18923184

    申请日：2024-10-22

    Applicant: Google LLC

    Inventor： Yuezhen Niu ,  Hartmut Neven ,  Vadim Smelyanskiy ,  Sergio Boixo Castrillo

    IPC: G06N10/00 ,  H03K3/38

    Abstract: Methods, systems, and apparatus for implementing a unitary quantum gate on one or more qubits. In one aspect, a method includes the actions designing a control pulse for the unitary quantum gate, comprising: defining a universal quantum control cost function, wherein the control cost function comprises a qubit leakage penalty term representing i) coherent qubit leakage, and ii) incoherent qubit leakage across all frequency components during a time dependent Hamiltonian evolution that realizes the unitary quantum gate; adjusting parameters of the time dependent Hamiltonian evolution to vary a control cost according to the control cost function such that leakage errors are reduced; generating the control pulse using the adjusted parameters; and applying the control pulse to the one or more qubits to implement the unitary quantum gate.

    QUANTUM STATISTIC MACHINE
    55.
    发明申请
    QUANTUM STATISTIC MACHINE 有权

    公开(公告)号：US20240412086A1

    公开(公告)日：2024-12-12

    申请号：US18404365

    申请日：2024-01-04

    Applicant: Google LLC

    Inventor： Masoud Mohseni ,  Hartmut Neven

    IPC: G06N10/00 ,  G06N20/00

    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.

    Classification using quantum neural networks
    56.
    发明授权
    Classification using quantum neural networks 有权

    公开(公告)号：US12001918B2

    公开(公告)日：2024-06-04

    申请号：US16962348

    申请日：2019-01-16

    Applicant: Google LLC

    Inventor： Edward Henry Farhi ,  Hartmut Neven

    IPC: G06N10/00 ,  G06N3/063 ,  G06N3/082 ,  G06N3/084

    CPC classification number: G06N10/00 ,  G06N3/063 ,  G06N3/082 ,  G06N3/084

    Abstract: This disclosure relates to classification methods that can be implemented on quantum computing systems. According to a first aspect, this specification describes a method for training a classifier implemented on a quantum computer, the method comprising: preparing a plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more readout qubits; applying one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state; determining, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state; comparing the predicted classification with the known classification; and updating one or more parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification.

    INHOMOGENEOUS QUANTUM ANNEALING SCHEDULES
    57.
    发明公开
    INHOMOGENEOUS QUANTUM ANNEALING SCHEDULES 审中-公开

    公开(公告)号：US20240078456A1

    公开(公告)日：2024-03-07

    申请号：US18463969

    申请日：2023-09-08

    Applicant: Google LLC

    Inventor： Masoud Mohseni ,  Hartmut Neven

    IPC: G06N10/00 ,  G06F9/48 ,  G06N10/60

    CPC classification number: G06N10/00 ,  G06F9/4881 ,  G06N10/60

    Abstract: Methods and apparatus for performing quantum annealing using a quantum system. In one aspect, a method includes controlling the quantum system such that a total Hamiltonian characterizing the quantum system evolves from an initial quantum Hamiltonian to a problem quantum Hamiltonian, wherein controlling the quantum system comprises applying an inhomogeneous driving field to the quantum system to drive the quantum system across a quantum phase transition.

    Quantum statistic machine
    58.
    发明授权
    Quantum statistic machine 有权

    公开(公告)号：US11900215B2

    公开(公告)日：2024-02-13

    申请号：US17678897

    申请日：2022-02-23

    Applicant: Google LLC

    Inventor： Masoud Mohseni ,  Hartmut Neven

    IPC: G06N10/00 ,  G06N20/00

    CPC classification number: G06N10/00 ,  G06N20/00

    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.

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