Gradient optimization of analytic controls: the route to high accuracy quantum optimal control

TL;DR

This paper introduces GOAT, a gradient-based optimal control method designed for high-accuracy quantum gate implementation, accommodating hardware constraints and simple parameterizations for superconducting qubits.

Contribution

The paper presents GOAT, a novel, simple, and flexible gradient optimization technique tailored for quantum control with hardware-specific constraints.

Findings

Successfully optimized fast, high-fidelity pulses for superconducting qubits.

Demonstrated GOAT's flexibility across different qubit architectures.

Achieved calibration-friendly control pulses with improved fidelity.

Abstract

Quantum computation places very stringent demands on gate fidelities, and experimental implementations require both the controls and the resultant dynamics to conform to hardware-specific constraints. Superconducting qubits present the additional requirement that pulses must have simple parameterizations, so they can be further calibrated in the experiment, to compensate for uncertainties in system parameters. Other quantum technologies, such as sensing, require extremely high fidelities. We present a novel, conceptually simple and easy-to-implement gradient-based optimal control technique named Gradient Optimization of Analytic conTrols (GOAT), which satisfies all the above requirements, unlike previous approaches. To demonstrate GOAT's capabilities, with emphasis on flexibility and ease of subsequent calibration, we optimize fast coherence-limited pulses for two leading superconducting qubits architectures - flux-tunable transmons and fixed-frequency transmons with tunable couplers.