REFINING QUBIT CALIBRATION MODELS USING SUPERVISED LEARNING

    公开(公告)号:US20230306292A1

    公开(公告)日:2023-09-28

    申请号:US18088303

    申请日:2022-12-23

    申请人: Google LLC

    IPC分类号: G06N5/04 G06N10/00 G06N20/00

    CPC分类号: G06N5/04 G06N10/00 G06N20/00

    摘要: A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.

    QUANTUM PHENOMENON-BASED OBFUSCATION OF MEMORY
    113.
    发明公开

    公开(公告)号:US20230306142A1

    公开(公告)日:2023-09-28

    申请号:US18295488

    申请日:2023-04-04

    IPC分类号: G06F21/79 G06N10/00 H04L9/06

    摘要: Systems, apparatuses, methods, and computer program products are disclosed for hardware-level encryption. An example method includes receiving an instance of information/data by processing circuitry; and disassembling, by the processing circuitry, the instance of information/data into a plurality of sections. The processing circuitry assigns each section of the plurality of sections a location in an allocated portion of memory. The locations are determined based at least in part on a quantum obfuscation map (QOM). The QOM is generated based on one or more quantum obfuscation elements (QOEs) corresponding to a quantum state of a quantum particle. The processing circuitry then causes each of the plurality of sections to be stored at the corresponding assigned location in the allocated portion of the memory.

    Training quantum evolutions using sublogical controls

    公开(公告)号:US11763187B2

    公开(公告)日:2023-09-19

    申请号:US18147144

    申请日:2022-12-28

    申请人: Google LLC

    摘要: 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 computing system and operation method thereof

    公开(公告)号:US11762733B2

    公开(公告)日:2023-09-19

    申请号:US17471591

    申请日:2021-09-10

    IPC分类号: G06F11/00 G06F11/10 G06N10/00

    CPC分类号: G06F11/1044 G06N10/00

    摘要: Disclosed is a quantum computing system including a first quantum chip including first physical qubits, a second quantum chip including second physical qubits, and a management device. The management device includes a physical qubit layer that manages physical qubit mapping including information about physical channels between first and second physical qubits, an abstraction qubit layer that manages abstraction qubit mapping including information about abstraction qubits and abstraction channels between the abstraction qubits based on the physical qubit mapping, a logical qubit layer that divides the abstraction qubits into logical qubits and to manage logical qubit mapping including information about logical channels between the logical qubits, based on the abstraction qubit mapping, and an application qubit layer that allocates at least one logical qubit corresponding to a qubit request received from a quantum application program based on the logical qubit mapping.

    Superconducting complex quantum computing circuit

    公开(公告)号:US11758829B2

    公开(公告)日:2023-09-12

    申请号:US17283037

    申请日:2019-10-31

    摘要: A superconducting complex quantum computing circuit includes a circuit substrate in which a wiring pattern of a circuit element including quantum bits and measurement electrodes, and ground patterns are formed, and through-electrodes connecting the ground pattern formed on a first surface of the substrate surface and the ground pattern formed on a second surface; a first ground electrode including a first contact portion in contact with the ground patterns, and a first non-contact portion having a shape corresponding to a shape of the wiring pattern; a second ground electrode including a second contact portion in contact with the ground pattern; a control signal line provided with a contact spring pin at a tip; and a pressing member that presses the first ground electrode against the first surface of the circuit substrate or presses the second ground electrode against the second surface of the circuit substrate.

    Quantum feature kernel alignment
    118.
    发明授权

    公开(公告)号:US11748665B2

    公开(公告)日:2023-09-05

    申请号:US16374354

    申请日:2019-04-03

    IPC分类号: G06N20/10 G06N10/00

    CPC分类号: G06N20/10 G06N10/00

    摘要: The illustrative embodiments provide a method, system, and computer program product for quantum feature kernel alignment using a hybrid classical-quantum computing system. An embodiment of a method for hybrid classical-quantum decision maker training includes receiving a training data set. In an embodiment, the method includes selecting, by a first processor, a sampling of objects from the training set, each object represented by at least one vector.
    In an embodiment, the method includes applying, by a quantum processor, a set of quantum feature maps to the selected objects, the set of quantum maps corresponding to a set of quantum kernels. In an embodiment, the method includes evaluating, by a quantum processor, a set of parameters for a quantum feature map circuit corresponding to at least one of the set of quantum feature maps.