Continuous quantum gate sets and pulse class meta-optimization

TL;DR

This paper introduces a method for learning families of optimal control pulses that adapt to various parameters, enabling the synthesis of continuous quantum gate classes with high fidelity, thus reducing circuit depth.

Contribution

It proposes a novel approach to pulse class meta-optimization that adaptively depends on parameters, improving quantum gate synthesis across diverse conditions.

Findings

Capable of producing high-fidelity pulses under parameter uncertainties

Effective on different experimentally relevant quantum gates

Reduces quantum circuit depth by optimizing gate synthesis

Abstract

Reducing the circuit depth of quantum circuits is a crucial bottleneck to enabling quantum technology. This depth is inversely proportional to the number of available quantum gates that have been synthesised. Moreover, quantum gate synthesis and control problems exhibit a vast range of external parameter dependencies, both physical and application-specific. In this article we address the possibility of learning families of optimal control pulses which depend adaptively on various parameters, in order to obtain a global optimal mapping from the space of potential parameter values to the control space, and hence continuous classes of gates. Our proposed method is tested on different experimentally relevant quantum gates and proves capable of producing high-fidelity pulses even in presence of multiple variable or uncertain parameters with wide ranges.