Differentiable Analog Quantum Computing for Optimization and Control

TL;DR

This paper introduces a differentiable analog quantum computing framework optimized for near-term devices, enabling scalable gradient-based training for quantum optimization and control with significant performance advantages.

Contribution

It presents the first differentiable analog quantum computing framework with pulse-level parameterization and a scalable gradient estimation method for quantum dynamics.

Findings

Significant performance improvements over digital quantum circuit methods.

Scalable quantum stochastic gradient descent algorithm.

Effective application to quantum optimization and control tasks.

Abstract

We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by orders of magnitude.