Job processing in quantum computing enabled cloud environments

    公开(公告)号:US12164954B2

    公开(公告)日:2024-12-10

    申请号:US17161838

    申请日:2021-01-29

    Abstract: A compatibility is ascertained between a configuration of a quantum processor (q-processor) of a quantum cloud compute node (QCCN) in a quantum cloud environment (QCE) and an operation requested in a first instruction in a portion (q-portion) of a job submitted to the QCE, the QCE including the QCCN and a conventional compute node (CCN), the CCN including a conventional processor configured for binary computations. In response to the ascertaining, a quantum instruction (q-instruction) is constructed corresponding to the first instruction. The q-instruction is executed using the q-processor of the QCCN to produce a quantum output signal (q-signal). The q-signal is transformed into a corresponding quantum computing result (q-result). A final result is returned to a submitting system that submitted the job, wherein the final result comprises the q-result.

    Providing reusable quantum circuit components as a curated service

    公开(公告)号:US11983471B2

    公开(公告)日:2024-05-14

    申请号:US17715234

    申请日:2022-04-07

    CPC classification number: G06F30/20 G06N10/00

    Abstract: A repository is configured in a hybrid data processing environment comprising a classical computing system and a quantum computing system, to hold a plurality of quantum circuit components (QCC(s)). A degree of difficulty in simulating the received QCC in the classical computing system is transformed into a classical hardness score. A degree of difficulty in implementing the received QCC in the quantum computing system is transformed into a quantum hardness score. A first parameter in a metadata data structure associated with the received QCC is populated with the classical hardness score. A second parameter in the metadata data structure associated with the received QCC is populated with the quantum hardness score. The received QCC is transformed into a library element by at least augmenting the received QCC with the metadata data structure. The library element is added to the repository.

    Quantum feature kernel alignment
    4.
    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

    CPC classification number: G06N20/10 G06N10/00

    Abstract: 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.

    Quantum circuit optimization using machine learning

    公开(公告)号:US11321625B2

    公开(公告)日:2022-05-03

    申请号:US16394133

    申请日:2019-04-25

    Abstract: A hybrid data processing environment comprising a classical computing system and a quantum computing system is configured. A configuration of a first quantum circuit is produced from the classical computing system, the first quantum circuit being executable using the quantum computing system. Using the quantum computing system, the first quantum circuit is executed. Using a pattern recognition technique, a portion of the first quantum circuit that can be transformed using a first transformation operation to satisfy a constraint on the quantum circuit design is identified. The portion is transformed to a second quantum circuit according to the first transformation operation, wherein the first transformation operation comprises reconfiguring a gate in the first quantum circuit such that a qubit used in the gate complies with the constraint on the quantum circuit design while participating in the second quantum circuit. Using the quantum computing system, the second quantum circuit is executed.

    Optimization of quantum circuits
    8.
    发明授权

    公开(公告)号:US11194946B2

    公开(公告)日:2021-12-07

    申请号:US16115828

    申请日:2018-08-29

    Abstract: A method for design optimization of a quantum circuit includes analyzing a first quantum circuit design based on at least one of a set of design criteria, wherein the first quantum circuit design includes a set of quantum logic gates, and wherein a design criterion in the set of design criteria includes changing a size of a matrix of transformations corresponding to a number of qubits employed in the first quantum circuit design. The embodiment further includes in the method modifying the first quantum circuit design into a transformed quantum circuit design, the modifying causing the transformed quantum circuit design to perform an operation implemented in the first quantum circuit design with a changed matrix of transformations.

    Cost function deformation in quantum approximate optimization

    公开(公告)号:US10963809B2

    公开(公告)日:2021-03-30

    申请号:US16840685

    申请日:2020-04-06

    Abstract: Techniques for performing cost function deformation in quantum approximate optimization are provided. The techniques include mapping a cost function associated with a combinatorial optimization problem to an optimization problem over allowed quantum states. A quantum Hamiltonian is constructed for the cost function, and a set of trial states are generated by a physical time evolution of the quantum hardware interspersed with control pulses. Aspects include measuring a quantum cost function for the trial states, determining a trial state resulting in optimal values, and deforming a Hamiltonian to find an optimal state and using the optimal state as a next starting state for a next optimization on a deformed Hamiltonian until an optimizer is determined with respect to a desired Hamiltonian.

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