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.

    BIAS CORRECTION IN DEEP LEARNING SYSTEMS
    5.
    发明申请

    公开(公告)号:US20200311525A1

    公开(公告)日:2020-10-01

    申请号:US16372136

    申请日:2019-04-01

    IPC分类号: G06N3/063 G06N3/08 G06F16/28

    摘要: In an embodiment, a method includes classifying, using a neural network including quantum components, a data set to generate a first set of classified data. In the embodiment, the method includes generating noise in the quantum components. In the embodiment, the method includes reclassifying, using the neural network, the data set with the generated noise to generate a second set of classified data. In the embodiment, the method includes determining, responsive to comparing the first set of classified data and the second set of classified data, a sensitivity of the quantum components.

    Prior knowledge-based topological feature classification

    公开(公告)号:US11586864B2

    公开(公告)日:2023-02-21

    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 WALK FOR COMMUNITY CLIQUE DETECTION

    公开(公告)号:US20210406954A1

    公开(公告)日:2021-12-30

    申请号:US16573862

    申请日:2019-09-17

    IPC分类号: G06Q30/02 G06N10/00

    摘要: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

    Quantum walk for community clique detection

    公开(公告)号:US11455562B2

    公开(公告)日:2022-09-27

    申请号:US16573862

    申请日:2019-09-17

    摘要: A method of detecting cliques in a graph includes determining, based on a number of nodes in the graph, a number of qubits to be included in a quantum processor. The method includes assigning to each node in the graph, a qubit of the quantum processor. The method includes operating on the qubits with a preparation circuit to create a quantum state in the qubits that corresponds to the graph. The method includes operating on the quantum state with a random walk circuit, and measuring the qubits of the quantum processor to detect cliques in the graph. The preparation circuit comprises a plurality of single- and two-qubit operators, wherein, for each pair of adjacent nodes in the graph, an operator of the plurality of two-qubit operators acts on a pair of qubits corresponding to the pair of adjacent nodes to create the quantum state.

    QUANTUM SPARSE FOURIER TRANSFORM
    10.
    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.