Training reinforcement machine learning systems

    公开(公告)号:US11663522B2

    公开(公告)日:2023-05-30

    申请号:US16859874

    申请日:2020-04-27

    CPC classification number: G06N20/00 G06F11/3466 G06F11/3664 G06N3/08 G06N10/70

    Abstract: A method of training a reinforcement machine learning computer system. The method comprises providing a machine-learning computer programming language including a pre-defined plurality of reinforcement machine learning criterion statements, and receiving a training specification authored in the machine-learning computer programming language. The training specification defines a plurality of training sub-goals with a corresponding plurality of the reinforcement machine learning criterion statements supported by the machine-learning computer programming language. The method further comprises computer translating the plurality of training sub-goals from the training specification into a shaped reward function configured to score a reinforcement machine learning model configuration with regard to the plurality of training sub-goals. The method further comprises running a training experiment with the reinforcement machine learning model configuration, scoring the reinforcement machine learning model in the training experiment with the shaped reward function, and adjusting the reinforcement machine learning model configuration based on the shaped reward function.

    Surface Codes with Densely Packed Gauge Operators

    公开(公告)号:US20240346356A1

    公开(公告)日:2024-10-17

    申请号:US18514579

    申请日:2023-11-20

    Applicant: Google LLC

    CPC classification number: G06N10/70

    Abstract: The disclosure is directed to implementing a quantum error correction code via a quantum computer that includes a set of functional qubits and a set of non-functional qubits. A set of gauge operators is formed. A set of gauge operator combinations are determined from the set of gauge operators. Determining the set of gauge operator combinations may be based on a subset of functional qubits and a global sequence of each gauge operator. Each gauge operator combination has a composite operator that commutes with the composite operator of each other gauge operator combination. A set of composite stabilizers may be generated. Each composite stabilizer corresponds to a separate gauge operator combination. The QEC code may be executed, via the QCS, based on the set of composite stabilizers.

    Fault correction for Clifford circuits

    公开(公告)号:US12112240B2

    公开(公告)日:2024-10-08

    申请号:US17820701

    申请日:2022-08-18

    CPC classification number: G06N10/70 H03M13/159 H03M13/611

    Abstract: A method to correct a fault in application of a Clifford circuit to a qubit register of a quantum computer comprises: (A) receiving circuit data defining the Clifford circuit; (B) emitting outcome code based on the circuit data, the outcome code including a series of outcome checks each corresponding to an anticipated error syndrome of the application of the Clifford circuit to the qubit register; and (C) emitting space-time quantum code corresponding to the Clifford circuit based on the circuit data and on the outcome code, the space-time quantum code including a series of check operators that support quantum-error correction, thereby enabling fault correction in the application of the Clifford circuit to the qubit register.

    DETERMINING DYNAMIC QUANTUM ERROR CORRECTION SCHEMES

    公开(公告)号:US20240289675A1

    公开(公告)日:2024-08-29

    申请号:US18520751

    申请日:2023-11-28

    CPC classification number: G06N10/70

    Abstract: A method, apparatus and product comprising: obtaining a logical representation of a quantum circuit, wherein the logical representation comprises a plurality of logical qubits manipulated by a plurality of logical gates; and generating a physical representation of the quantum circuit, the physical representation is configured to allocate a set of physical qubits of a quantum computer to the plurality of logical qubits in order to implement error correction operations The generating includes selecting a first quantity of physical qubits from the set of physical qubits for a first separate section of the quantum circuit; selecting a second quantity of physical qubits from the set of physical qubits for a second separate section of the quantum circuit, and synthesizing the quantum circuit using the first and second quantities for the first and second separate sections.

    FAST MINIMUM-WEIGHT PERFECT MATCHING (MWPM) DECODER FOR QUANTUM ERROR CORRECTION

    公开(公告)号:US20240273393A1

    公开(公告)日:2024-08-15

    申请号:US18437441

    申请日:2024-02-09

    CPC classification number: G06N10/70 G06N10/60

    Abstract: Provided herein are methods of solving minimum-weight perfect matching (MWPM) for quantum error correction. The methods include i) partitioning an algorithm into a dual module and a primal module; ii) computing a maximum update length in the dual module; iii) gathering a return value in the dual module, the return value including a growth event or a conflict event; iv) resolving a conflict in the primal module when the return value is the conflict event; and v) setting a growth state in the dual module for each of one or more nodes when the return value is the growth event.

    Multi-exponential error extrapolation

    公开(公告)号:US12061953B2

    公开(公告)日:2024-08-13

    申请号:US17926146

    申请日:2021-07-01

    CPC classification number: G06N10/70 G06F11/0721 G06F11/079

    Abstract: A method of mitigating errors when using a quantum computer comprising: performing S101 a first operation (21) on the state of a qubit a plurality of times; wherein the first operation (21) has a first error rate (32); obtaining S102 a first measurement of the average state of the qubit; modifying S103 the error rate of the quantum computer from the first error rate (32) to a second error rate (34); performing S104 a second operation (23) on the state of the qubit a plurality of times; wherein the second operation (23) has the second error rate (34); obtaining S105 a second measurement of the average state of the qubit; modifying S106 the error rate of the quantum computer from the second error rate to a third error rate; performing S107 a third operation on the state of the qubit a plurality of times; wherein the third operation has the third error rate; obtaining S108 a third measurement of the average state of the qubit; modifying S109 the error rate of the quantum computer from the third error rate to a fourth error rate; performing S110 a fourth operation on the state of the qubit a plurality of times; wherein the fourth operation has the fourth error rate; obtaining S111 a fourth measurement of the average state of the qubit; fitting S112 the first, second, third and fourth measurements to a multi-exponential decay curve (35); and extrapolating S113 the average state of the qubit at a fifth error rate (37) using the fitted curve (35), wherein the fifth error rate (37) is lower than the first, second, third and fourth error rates.

    Method for transmitting information through topological quantum error correction based on multi-space-time transformation

    公开(公告)号:US12039412B2

    公开(公告)日:2024-07-16

    申请号:US17873280

    申请日:2022-07-26

    CPC classification number: G06N10/70 G06N10/20

    Abstract: A method and apparatus for transmitting information through a topological quantum error correction system based on multi-space-time transformation including steps of initializing quantum information, detecting an error in quantum information transmission, correcting the error in quantum information transmission, and decoding the information in quantum information transmission. Information safety is improved and only a quantity of devices for generating quantum states needs to be increased. A stabilizer is used to analyze code symmetry, for error detection, measurement, and correction. Any information about an encoded qubit is not revealed during odd/even parity measurement, so that an encoding state of the encoded qubit remains unchanged. A double-layer convolutional neural network model in an adversarial network can find an error correction chain with a best effect.

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