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    Quantum statistic machine
    74.
    发明授权
    Quantum statistic machine 有权

    公开(公告)号：US12210933B2

    公开(公告)日：2025-01-28

    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.

    QUANTUM COMPUTATION THROUGH REINFORCEMENT LEARNING
    75.
    发明申请
    QUANTUM COMPUTATION THROUGH REINFORCEMENT LEARNING 有权

    公开(公告)号：US20240394530A1

    公开(公告)日：2024-11-28

    申请号：US18428284

    申请日：2024-01-31

    Applicant: Google LLC

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

    IPC: G06N3/08 ,  G06N10/00

    Abstract: Methods, systems, and apparatus for designing a quantum control trajectory for implementing a quantum gate using quantum hardware. In one aspect, a method includes the actions of representing the quantum gate as a sequence of control actions and applying a reinforcement learning model to iteratively adjust each control action in the sequence of control actions to determine a quantum control trajectory that implements the quantum gate and reduces leakage, infidelity and total runtime of the quantum gate to improve its robustness of performance against control noise during the iterative adjustments.

    QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE
    76.
    发明公开
    QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE 审中-公开

    公开(公告)号：US20240303502A1

    公开(公告)日：2024-09-12

    申请号：US18281497

    申请日：2022-03-10

    Applicant: Google LLC

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

    IPC: G06N3/094 ,  G06N10/60

    CPC classification number: G06N3/094 ,  G06N10/60

    Abstract: Methods and apparatus for learning a target quantum state. In one aspect, a method for training a quantum generative adversarial network (QGAN) to learn a target quantum state includes iteratively adjusting parameters of the QGAN until a value of a QGAN loss function converges, wherein each iteration comprises: performing an entangling operation on a discriminator network input of a discriminator network in the QGAN to measure a fidelity of the discriminator network input, wherein the discriminator network input comprises the target quantum state and a first quantum state output from a generator network in the QGAN, wherein the first quantum state approximates the target quantum state; and performing a minimax optimization of the QGAN loss function to update the QGAN parameters, wherein the QGAN loss function is dependent on the measured fidelity of the discriminator network input.

    CLASSIFICATION USING QUANTUM NEURAL NETWORKS
    77.
    发明公开
    CLASSIFICATION USING QUANTUM NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240296359A1

    公开(公告)日：2024-09-05

    申请号：US18648052

    申请日：2024-04-26

    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.

    Quantum computation through reinforcement learning
    80.
    发明授权
    Quantum computation through reinforcement learning 有权

    公开(公告)号：US11928586B2

    公开(公告)日：2024-03-12

    申请号：US16962059

    申请日：2018-01-31

    Applicant: Google LLC

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

    IPC: G06N3/08 ,  G06N10/00

    CPC classification number: G06N3/08 ,  G06N10/00

    Abstract: Methods, systems, and apparatus for designing a quantum control trajectory for implementing a quantum gate using quantum hardware. In one aspect, a method includes the actions of representing the quantum gate as a sequence of control actions and applying a reinforcement learning model to iteratively adjust each control action in the sequence of control actions to determine a quantum control trajectory that implements the quantum gate and reduces leakage, infidelity and total runtime of the quantum gate to improve its robustness of performance against control noise during the iterative adjustments.

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