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Computational Mathematics and Mathematical Physics

Efficient Machine Learning Generation of Target Quantum Processes

I. T. Russkikh, Boris Olegovich Volkov, Alexander Nikolaevich Pechen

Published Aug 1, 2026• 0 citations
Publisher

Abstract

Abstract This work addresses the problem of developing a machine-learning approach for the generation of a desired quantum process, i.e., a predefined trajectory in the set of quantum channels. To this end, a physical model of N-level quantum systems driven by coherent and incoherent controls is constructed, in which both Hamiltonian and dissipative dynamics are controlled. The main challenge in generating a target quantum process is the high complexity of finding optimal controls with conventional local-search optimization methods. To efficiently solve this problem, we develop a machine learning method that leverages a special representation of the dynamics. The proposed machine learning method achieves high efficiency and fast execution times: it delivers significantly higher accuracy (by an order of magnitude) and accelerates the generation of target quantum processes compared to standard local search methods for qubit, qutrit, and qudit (five-level) systems.

DOI
10.1134/s0965542526700582

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