US 12,585,973 B1Granted

Adaptive calibration for quantum computing systems

AssigneeQpiAI India Private Limited

InventorsAswanth Krishnan, Lakshya Priyadarshi, Manjunath Ramachandrappa Venkatesh, Nagendra Nagaraja

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Abstract

Systems and methods for adaptive calibration of quantum computing systems are disclosed. A system can include one or more processors coupled to non-transitory memory and configured to obtain telemetry data from a quantum processor of a quantum computing system, generate a spatio-temporal graph data structure based on the telemetry data and operational parameters, and provide at least a portion of the graph as input to a graph neural network (GNN) to generate parameter ranges for operation. The system can select a first set of test parameters using a Bayesian optimization function, execute calibration experiments to generate calibration results, generate updated operational parameters based on the results, and control the quantum processor according to the updated parameters.

Claims20

Dependent claims

A system, comprising:

one or more processors coupled to non-transitory memory, the one or more processors configured to:
obtain telemetry data from a quantum processor of a quantum computing system, the telemetry data corresponding to a first set of operational parameters;
generate a spatio-temporal graph data structure based on the telemetry data and the first set of operational parameters, wherein a set of nodes of the spatio-temporal graph data structure correspond to a set of qubits of the quantum processor, the spatio-temporal graph data structure comprising (i) a first set of edges corresponding to spatial relationships between the set of qubits, and (ii) a second set of edges corresponding to temporal relationships between the set of nodes;
provide at least a portion of the spatio-temporal graph data structure as input to a graph neural network (GNN) to generate a set of parameter ranges for operation of the quantum processor;
select, based on the set of parameter ranges, a first set of test parameters for the quantum computing system using a Bayesian optimization function;
execute one or more calibration experiments using the first set of test parameters to generate a set of calibration results;
generate a second set of operational parameters for the quantum computing system based on the set of calibration results;
and
control the quantum processor according to the second set of operational parameters.

6 more elements

A method, comprising:

obtaining, by one or more processors coupled to non-transitory memory, telemetry data from a quantum processor of a quantum computing system, the telemetry corresponding to a first set of operational parameters;
generating, by the one or more processors, a spatio-temporal graph data structure based on the telemetry data and the first set of operational parameters, wherein a set of nodes of the spatio-temporal graph data structure correspond to a set of qubits of the quantum processor, the spatio-temporal graph data structure comprising (i) a first set of edges corresponding to spatial relationships between the set of qubits, and (ii) a second set of edges corresponding to temporal relationships between the set of nodes;
providing, by the one or more processors, at least a portion of the spatio-temporal graph data structure as input to a graph neural network (GNN) to generate a set of parameter ranges for operation of the quantum processor;
selecting, by the one or more processors, based on the set of parameter ranges, a first set of test parameters for the quantum computing system using a Bayesian optimization function;
executing, by the one or more processors, one or more calibration experiments using the first set of test parameters to generate a set of calibration results;
generating, by the one or more processors, a second set of operational parameters for the quantum computing system based on the set of calibration results;
and
controlling, by the one or more processors, the quantum processor according to the second set of operational parameters.

5 more elements

Dependencies are inferred from the claim text.