QUANTUM SPARSE FOURIER TRANSFORM
    2.
    发明申请

    公开(公告)号:US20220107989A1

    公开(公告)日:2022-04-07

    申请号:US17065277

    申请日:2020-10-07

    IPC分类号: G06F17/14 G06N10/00

    摘要: A method for performing sparse quantum Fourier transform computation includes defining a set of quantum circuits, each quantum circuit comprising a Hadamard gate and a single frequency rotation operator, said set of quantum circuits being equivalent to a quantum Fourier transform circuit. The method includes constructing a subset of said quantum circuits in a quantum processor, said quantum processor having a quantum representation of a classical distribution loaded into a quantum state of said quantum processor. The method includes executing said subset of said quantum circuits on said quantum state, and performing a measurement in a frequency basis to obtain a frequency distribution corresponding to said quantum state.

    QUANTUM CIRCUIT FOR TRANSFORMATION OF MIXED STATE VECTORS

    公开(公告)号:US20240020563A1

    公开(公告)日:2024-01-18

    申请号:US17863449

    申请日:2022-07-13

    IPC分类号: G06N10/40 G06N10/20

    CPC分类号: G06N10/40 G06N10/20

    摘要: Systems and methods for operating quantum systems are described. A controller of a quantum system can generate a command signal. The quantum system can include quantum hardware having a plurality of qubits. An interface of the quantum system can control the quantum hardware based on the command signal to sample an input vector represented by the first set of qubits, where the input vector includes mixed states with different Hamming weights. The interface can control the quantum hardware to entangle the first set of qubits to the second set of qubits, where the second set of qubits represent a count of nonzero elements in the input vector. The interface can control the quantum hardware to generate an output vector based on the entanglement of the first set of qubits to the second set of qubits, where the output vector includes one or more states having a specific Hamming weight.

    COMBINING DOMAIN-SPECIFIC ONTOLOGIES FOR LANGUAGE PROCESSING

    公开(公告)号:US20220284996A1

    公开(公告)日:2022-09-08

    申请号:US17188310

    申请日:2021-03-01

    摘要: Methods and systems for performing a natural language processing task include identifying hypernym/hyponym relations in a depth-wise ontology and identifying synonymy relations in a breadth-wise ontology. The depth-wise ontology and the breadth-wise ontology are combined into a combined ontology using the identified hypernym/hyponym relations and the identified synonymy relations. Enhanced hypernym/hyponym relations are embedded using the combined ontology. A natural language processing task is performed using the enhanced hypernym/hyponym relations and the combined ontology.

    PRIOR KNOWLEDGE-BASED TOPOLOGICAL FEATURE CLASSIFICATION

    公开(公告)号:US20200265274A1

    公开(公告)日:2020-08-20

    申请号:US16276719

    申请日:2019-02-15

    摘要: Techniques regarding topological classification of complex datasets are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a quantum computing component that can encode eigenvalues of a Laplacian matrix into a phase on a quantum state of a quantum circuit. The computer executable components can also comprise a classical computing component that infers a Betti number using a Bayesian learning algorithm by measuring an ancilla state of the quantum circuit.

    QUANTUM TOPOLOGICAL CLASSIFICATION

    公开(公告)号:US20210256414A1

    公开(公告)日:2021-08-19

    申请号:US16576046

    申请日:2019-09-19

    摘要: Systems, computer-implemented methods, and computer program products that can facilitate quantum topological classification are described. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a topological component that employs one or more quantum computing operations to identify one or more persistent homology features of a topological simplicial structure. The computer executable components can further comprise a topological classifier component that employs one or more machine learning models to classify the topological simplicial structure based on the one or more persistent homology features.