Using optimal control to guide neural-network interpolation of continuously-parameterized gates

TL;DR

This paper presents a method combining quantum optimal control and physics-informed neural networks to efficiently interpolate and calibrate continuously-parameterized quantum gates, improving speed and robustness for quantum algorithms.

Contribution

It introduces a novel approach that uses optimal control to guide neural network training for synthesizing nonlinear control surfaces of quantum gates, surpassing linear interpolation methods.

Findings

Achieves rapid convergence to control surfaces for quantum gates.

Enables transfer learning for device calibration over time.

Demonstrates potential for 3x speedups in quantum algorithms.

Abstract

Control synthesis for continuously-parameterized families of quantum gates can enable critical advantages for mid-sized quantum computing applications in advance of fault-tolerance. We combine quantum optimal control with physics-informed machine learning to efficiently synthesize control surfaces that interpolate among continuously-parameterized gate families. Using optimal control as an active learning strategy to guide pretraining, we bootstrap a physics-informed neural network to achieve rapid convergence to nonlinear control surfaces sufficient for our desired gates. We find our approach is critical for enabling an expressiveness beyond linear interpolation, which is important in cases of hard quantum control. We show in simulation that by adapting our pretraining to use a few reference pulse calibrations, we can apply transfer learning to quickly calibrate our learned control surfaces when devices fluctuate over time. We demonstrate synthesis for one and two qubit gates with one or two parameters, focusing on gate families for variational quantum algorithm (VQA) ansatz. By avoiding the inefficient decomposition of VQA ansatz into basis gate sets, continuous gate families are a potential method to improve the noise robustness of VQAs in the near term. Our framework shows how accessible optimal control tools can be combined with simple machine learning to enable practitioners to achieve 3x speedups for their algorithms by going beyond the standard gate sets.