Enhanced Quantum Synchronization via Quantum Machine Learning
Abstract
We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in this digital-analog block decomposition the fundamental elements of a quantum machine learning protocol, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If we can additionally equip this algorithm with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state and local conditional operations on the agent and environment subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experience different loss/decoherence mechanisms, and give us the flexibility to choose the synchronization state. Finally, we propose an implementation based on current technologies in superconducting circuits.
Cite
@article{arxiv.1709.08519,
title = {Enhanced Quantum Synchronization via Quantum Machine Learning},
author = {F. A. Cárdenas-López and M. Sanz and J. C. Retamal and E. Solano},
journal= {arXiv preprint arXiv:1709.08519},
year = {2019}
}