Quantum Time-Series Learning with Evolutionary Algorithms

• Published in: arXiv.org
• Main Authors: Vignesh Anantharamakrishnan, Taddei, Márcio M
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 23.12.2024
• Subjects: Evolutionary algorithms; Machine learning; Optimization; Parameter sensitivity; Quantum computing; Time series
• ISSN: 2331-8422

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.