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arXiv · 2412.17580

Quantum Time-Series Learning with Evolutionary Algorithms

Vignesh Anantharamakrishnan·Márcio M. Taddei
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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.

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Vignesh Anantharamakrishnan, Márcio M. Taddei. 2024-12-23. Quantum Time-Series Learning with Evolutionary Algorithms. https://arxiv.org/abs/2412.17580

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