We present numerical simulation of a six-qubit quantum reservoir network with an output implemented on a 5-dimensional decoherence-free subspace (DFS), working as a classifier between entangled and product states of the input quantum system, fed to the reservoir during a finite learning time. Since the dynamics of DFS is not affected by external fluctuations, no cooling is required, and the proposed model seems a promising candidate for future quantum artificial intelligence systems working at room temperatures and free of huge energy consumption.
@article{arxiv.2605.27427,
title = {Quantum reservoir networks based on decoherence-free subspaces},
author = {V. V. Akshay and M. V. Altaisky and N. E. Kaputkina},
journal= {arXiv preprint arXiv:2605.27427},
year = {2026}
}