Supervised learning for robust quantum control in composite-pulse systems

TL;DR

This paper introduces a supervised learning approach to design robust quantum control in composite-pulse systems, enhancing fidelity and robustness against systematic errors through a modified gradient descent algorithm.

Contribution

It presents a novel supervised learning model with a modified gradient descent for robust quantum control, adaptable to various physical parameters and error types.

Findings

The model resists systematic errors including time-varying errors.

Increasing training parameters improves fidelity and robustness.

Application to quantum gates and superposition states demonstrates effectiveness.

Abstract

In this work, we develop a supervised learning model for implementing robust quantum control in composite-pulse systems, where the training parameters can be either phases, detunings, or Rabi frequencies. This model exhibits great resistance to all kinds of systematic errors, including single, multiple, and time-varying errors. We propose a modified gradient descent algorithm for adapting the training of phase parameters, and show that different sampling methods result in different robust performances. In particular, there is a trade-off between high fidelity and robustness for a given number of training parameters, and both can be simultaneously enhanced by increasing the number of training parameters (pulses). For its applications, we demonstrate that the current model can be used for achieving high-fidelity arbitrary superposition states and universal quantum gates in a robust manner. This work provides a highly efficient learning model for fault-tolerant quantum computation by training various physical parameters.