Systems and Methods to Learn Two-Level System Defects in Quantum Systems
AssigneeGoogle Inc.
InventorsYuezhen Niu, Vadim Smelyanskiy, Sergio Boixo Castrillo, Paul Victor Klimov
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Abstract
Example aspects of the present disclosure provide systems and methods to learn machine-learned model parameters for models of quantum computing systems. In particular, example aspects of the present disclosure are directed to systems and methods to learn a deep neural network configured to predict parameter values for a physical model that models quantum dynamics of interactions between one or more qubits of a quantum gate and one or more two-level-system (TLS) defects during operation of the quantum gate through use of an evolutionary algorithm.
Claims1
A computer-implemented method for training a machine-learned parameter value prediction model configured to predict parameter values for parameters of a physical model of a quantum computing system, comprising:
- obtaining, by a computing system comprising one or more classical processors, a set of experimental data descriptive of a performance of the quantum computing system;
- obtaining, by the computing system, an output of the physical model based at least in part on a set of experimental inputs;
- determining, by the computing system, a comparison of the output of the physical model to the data descriptive of the performance of the quantum computing system;
- training, by the computing system, one or more parameters of the machine-learned parameter value prediction model using an evolutionary algorithm based at least in part on the comparison.
Dependencies are inferred from the claim text.