Quantum Natural Stochastic Pairwise Coordinate Descent

TL;DR

This paper introduces a new quantum optimization algorithm that uses a novel information metric and single-shot measurements to improve convergence speed and sample efficiency in variational quantum algorithms.

Contribution

A novel quantum information metric and an unbiased estimator enable a more efficient quantum optimization algorithm that avoids full-state tomography.

Findings

Better sample complexity demonstrated experimentally

Faster convergence compared to existing methods

Ability to avoid saddle points and local minima

Abstract

Variational quantum algorithms, optimized using gradient-based methods, often exhibit sub-optimal convergence performance due to their dependence on Euclidean geometry. Quantum natural gradient descent (QNGD) is a more efficient method that incorporates the geometry of the state space via a quantum information metric. However, QNGD is computationally intensive and suffers from high sample complexity. In this work, we formulate a novel quantum information metric and construct an unbiased estimator for this metric using single-shot measurements. We develop a quantum optimization algorithm that leverages the geometry of the state space via this estimator while avoiding full-state tomography, as in conventional techniques. We provide the convergence analysis of the algorithm under mild conditions. Furthermore, we provide experimental results that demonstrate the better sample complexity and faster convergence of our algorithm compared to the state-of-the-art approaches. Our results illustrate the algorithm's ability to avoid saddle points and local minima.