Fast Quantum Process Tomography via Riemannian Gradient Descent

TL;DR

This paper introduces a Riemannian gradient descent method for quantum process tomography, achieving faster, more accurate results with incomplete data, suitable for high-dimensional quantum systems.

Contribution

It presents a novel stochastic Riemannian optimization algorithm tailored for quantum process tomography, outperforming traditional methods in speed and accuracy.

Findings

Achieves order-of-magnitude faster quantum process characterization

Supports incomplete measurement data effectively

Demonstrated on both simulations and quantum hardware

Abstract

Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science and becomes especially challenging for high-dimensional complex structure problems. One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data. In this paper, we introduce a modified version of stochastic gradient descent on a Riemannian manifold that integrates recent advancements in numerical methods for Riemannian optimization. This approach inherently supports the physically driven constraints of a quantum process, takes advantage of state-of-the-art large-scale stochastic objective optimization, and has superior performance to traditional approaches such as maximum likelihood estimation and projected least squares. The data-driven approach enables accurate, order-of-magnitude faster results, and works with incomplete data. We demonstrate our approach on simulations of quantum processes and in hardware by characterizing an engineered process on quantum computers.