US Patent Application 17738642. Iterative Supervised Learning of Quantum Processor Error Models simplified abstract
Contents
Iterative Supervised Learning of Quantum Processor Error Models
Organization Name
Inventor(s)
Paul Victor Klimov of Santa Barbara CA (US)
Iterative Supervised Learning of Quantum Processor Error Models - A simplified explanation of the abstract
This abstract first appeared for US patent application 17738642 titled 'Iterative Supervised Learning of Quantum Processor Error Models
Simplified Explanation
- This patent application describes systems and methods for generating error models for quantum algorithms implemented on quantum processors. - The method involves obtaining data associated with a benchmark model, which includes error indicators, benchmarks, and trainable parameters. - Each error indicator is associated with a distinct quantum gate calibrated in a distinct operating configuration. - The method determines parameter values for the trainable parameters. - The quantum computing system operates based on the determined parameter values.
Original Abstract Submitted
Systems and methods for generating error models for quantum algorithms implemented on quantum processors having a plurality of qubits are provided in one example, a method includes obtaining data associated with a benchmark model, the benchmark model having one or more error indicators as features, one or more benchmarks as targets, and one or more trainable parameters, wherein each error indicator is associated with a distinct quantum gate calibrated in a distinct operating configuration associated with a plurality of operating parameters for the quantum gate and associated with a calibration data for the operating configuration. The method includes determining parameter values for the trainable parameters. The method include operating a quantum computing system based on operating parameters determined based on the parameter values.
