Quantum Time-Series Learning with Evolutionary Algorithms

TL;DR

This paper demonstrates that evolutionary algorithms can outperform gradient descent in optimizing variational quantum circuits for time-series forecasting, avoiding local minima and significantly improving prediction accuracy.

Contribution

It introduces the use of evolutionary algorithms for quantum circuit parameter optimization in time-series forecasting, showing their advantages over traditional gradient-based methods.

Findings

Evolutionary algorithms avoid local minima better than gradient descent.

Up to six-fold reduction in prediction error with evolutionary methods.

Hybrid approaches combining both methods are explored.

Abstract

Variational quantum circuits have arisen as an important method in quantum computing. A crucial step of it is parameter optimization, which is typically tackled through gradient-descent techniques. We advantageously explore instead the use of evolutionary algorithms for such optimization, specifically for time-series forecasting. We perform a comparison, for diverse instances of real-world data, between gradient-descent parameter optimization and covariant-matrix adaptation evolutionary strategy. We observe that gradient descent becomes permanently trapped in local minima that have been avoided by evolutionary algorithms in all tested datasets, reaching up to a six-fold decrease in prediction error. Finally, the combined use of evolutionary and gradient-based techniques is explored, aiming at retaining advantages of both. The results are particularly applicable in scenarios sensitive to gains in accuracy.