Enhanced Quantum Synchronization via Quantum Machine Learning

TL;DR

This paper demonstrates that digital-analog quantum decomposition can enhance quantum synchronization between two-level systems and introduces a quantum machine learning protocol that improves synchronization robustness and flexibility, with practical superconducting circuit implementation.

Contribution

It presents a novel digital-analog approach to quantum synchronization and integrates quantum machine learning elements to enhance robustness and control.

Findings

Digital-analog decomposition induces quantum synchronization.

The protocol is robust against loss and decoherence.

Superconducting circuits can implement the proposed scheme.

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.