Interplay between Quantum Machine Learning and Open Quantum Systems
Quantum Machine Learning models (QML) aim to exploit unique quantum properties such as coherence and entanglement to enhance machine learning algorithms. Traditional approaches typically focus on optimizing the unitary evolution of quantum systems, while non-unitary elements, like decoherence, are viewed as detrimental. We propose a paradigm shift by exploring the potential of controlling non-unitary dynamics through the framework of open quantum systems. Utilizing both numerical and analytical methods, we demonstrate that our approach offers significant advantages over existing QML models. Specifically, we show that quantum dissipation can enhance the performances of quantum reservoir computers [1] and variational quantum algorithms [2].
[1] A. Sannia, R. Martínez-Peña, M. C. Soriano, G. L. Giorgi, and R. Zambrini, Dissipation as a resource for quantum reservoir computing, Quantum 8, 1291 (2024)
[2] A. Sannia, F. Tacchino, I. Tavernelli, G. L. Giorgi, and R. Zambrini, Engineered dissipation to mitigate barren plateaus, arXiv preprint arXiv:2310.15037 (2023).
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