Multiple Classical Noise Mitigation by Multiobjective Robust Quantum Optimal Control

TL;DR

This paper presents a multiobjective robust quantum optimal control method to design control pulses that can resist multiple classical noise sources, improving quantum gate fidelity in noisy environments.

Contribution

It introduces a gradient-based multiobjective optimization approach for robust quantum control against multiple classical noises, demonstrated on trapped ion and superconducting qubit systems.

Findings

Robust control pulses effectively resist multiple static and dynamic noises.

High-fidelity quantum gates achieved under realistic noisy conditions.

Method applicable to various quantum computing platforms.

Abstract

High-quality control is a fundamental requirement for quantum computation, but practically it is often hampered by the presence of various types of noises, which can be static or time-dependent. In many realistic scenarios, multiple noise sources coexist, and their resulting noise effects need be corrected to a sufficient order, posing significant challenges for the design of effective robust control methods. Here, we explore the method of robust quantum optimal control to generally tackle the problem of resisting multiple noises from a complicated noise environment. Specifically, we confine our analysis to unitary noises that can be described by classical noise models. This method employs a gradient-based multiobjective optimization algorithm to maximize the control figure of merit, and meanwhile to minimize the perturbative effects of the noises that are allowed for. To verify its effectiveness, we apply this method to a number of examples, including roubust entangling gate in trapped ion system and robust controlled-Z gate in superconducting qubits, under commonly encountered static and time-dependent noises. Our simulation results reveal that robust optimal control can find smooth, robust pulses that can simultaneously resist several noises and thus achieve high-fidelity gates. Therefore, we expect that this method will find wide applications on current noisy quantum computing devices.