Robust Quantum Optimal Control with Trajectory Optimization

TL;DR

This paper introduces robust trajectory optimization methods for quantum control, enabling high-fidelity gate implementation resilient to systematic errors and decoherence, with efficient computation and application to fluxonium qubits.

Contribution

It presents a derivative-based robust control approach using trajectory optimization and efficient modeling to improve quantum gate fidelity under uncertainties.

Findings

Suppressed gate errors below 10^{-7} for 1% parameter deviations.

Achieved high-fidelity gates with computationally efficient methods.

Applied techniques successfully to fluxonium qubits.

Abstract

The ability to engineer high-fidelity gates on quantum processors in the presence of systematic errors remains the primary barrier to achieving quantum advantage. Quantum optimal control methods have proven effective in experimentally realizing high-fidelity gates, but they require exquisite calibration to be performant. We apply robust trajectory optimization techniques to suppress gate errors arising from system parameter uncertainty. We propose a derivative-based approach that maintains computational efficiency by using forward-mode differentiation. Additionally, the effect of depolarization on a gate is typically modeled by integrating the Lindblad master equation, which is computationally expensive. We employ a computationally efficient model and utilize time-optimal control to achieve high-fidelity gates in the presence of depolarization. We apply these techniques to a fluxonium qubit and suppress simulated gate errors due to parameter uncertainty below $10^{-7}$ for static parameter deviations on the order of $1\%$.