Extremum seeking control of quantum gates

TL;DR

This paper introduces a data-driven extremum-seeking control method combined with randomized benchmarking to stabilize two-qubit gates in trapped ion quantum computers, improving gate fidelity amid control fluctuations.

Contribution

It presents a novel integration of extremum-seeking control with randomized benchmarking for real-time stabilization of quantum gates.

Findings

Successful stabilization of two-qubit gates in simulation.

Experimental validation on a commercial trapped-ion quantum computer.

Enhanced gate fidelity under control parameter fluctuations.

Abstract

To be useful for quantum computation, gate operations must be maintained at high fidelities over long periods of time. In addition to decoherence, slow drifts in control hardware leads to inaccurate gates, causing the quality of operation of as-built quantum computers to vary over time. Here, we demonstrate a data-driven approach to stabilized control, combining extremum-seeking control (ESC) with direct randomized benchmarking (DRB) to stabilize two-qubit gates under unknown control parameter fluctuations. As a case study, we consider these control strategies in the context of a trapped ion quantum computer using physically-realistic simulation. We then experimentally demonstrate this control strategy on a state-of-the-art, commercial trapped-ion quantum computer.