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    Coupling architectures for superconducting flux qubits
    94.
    发明授权
    Coupling architectures for superconducting flux qubits 有权

    公开(公告)号：US10832156B2

    公开(公告)日：2020-11-10

    申请号：US16096110

    申请日：2016-12-30

    Applicant: Google LLC

    Inventor： Yu Chen ,  Hartmut Neven ,  Austin Greig Fowler ,  Alireza Shabani Barzegar

    IPC: G06N10/00 ,  H01L39/02 ,  H01P3/00 ,  H01P7/08

    Abstract: A quantum computing device includes: a first array of qubits arranged along a first axis; and a second array of qubits arranged along a second axis different from the first axis so that the qubits of the second array intersect with the qubits of the first array to form a lattice structure, in which each qubit in the first array is offset along the second axis relative to a directly adjacent qubit in the first array, each qubit in the second array is offset along the first axis relative to a directly adjacent qubit in the second array, and each intersection between a qubit from the first array and a qubit from the second array in the lattice structure comprises a coupler arranged to inductively couple the qubit from the first array to the qubit from the second array.

    CLASSIFICATION USING QUANTUM NEURAL NETWORKS
    95.
    发明申请
    CLASSIFICATION USING QUANTUM NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200342345A1

    公开(公告)日：2020-10-29

    申请号：US16962348

    申请日：2019-01-16

    Applicant: Google LLC

    Inventor： Edward Henry Farhi ,  Hartmut Neven

    IPC: G06N10/00 ,  G06N3/063 ,  G06N3/08

    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 NEURAL NETWORK
    100.
    发明申请
    QUANTUM NEURAL NETWORK 审中-公开

    公开(公告)号：US20200169396A1

    公开(公告)日：2020-05-28

    申请号：US16618713

    申请日：2018-06-01

    Applicant: Google LLC

    Inventor： Hartmut Neven ,  Edward Henry Farhi

    IPC: H04L9/08 ,  G06N3/063 ,  G06N3/08 ,  G06N20/00 ,  G06N10/00 ,  B82Y10/00

    Abstract: A quantum neural network architecture. In one aspect, a quantum neural network trained to perform a machine learning task includes: an input quantum neural network layer comprising (i) multiple qubits prepared in an initial quantum state encoding a machine learning task data input, and (ii) a target qubit; a sequence of intermediate quantum neural network layers, each intermediate quantum neural network layer comprising multiple quantum logic gates that operate on the multiple qubits and target qubit; and an output quantum neural network layer comprising a measurement quantum gate that operates on the tar get qubit and provides as output data representing a solution to the machine learning task.

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