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Enhanced Quantum Synchronization via Quantum Machine Learning

Enhanced Quantum Synchronization via Quantum Machine Learning The quantum synchronization between a pair of two‐level systems inside two coupled cavities is studied. By using a digital–analog decomposition of the master equation that rules the system dynamics, it is shown that this approach leads to quantum synchronization between both two‐level systems. Moreover, in this digital–analog block decomposition, the fundamental elements of a quantum machine learning protocol can be identified, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If the algorithm can be additionally equipped 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 experiences different loss/decoherence mechanisms, and gives the flexibility to choose the synchronization state. Finally, an implementation is proposed, based on current technologies in superconducting circuits. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advanced Quantum Technologies Wiley

Enhanced Quantum Synchronization via Quantum Machine Learning

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Publisher
Wiley
Copyright
© 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim
eISSN
2511-9044
DOI
10.1002/qute.201800076
Publisher site
See Article on Publisher Site

Abstract

The quantum synchronization between a pair of two‐level systems inside two coupled cavities is studied. By using a digital–analog decomposition of the master equation that rules the system dynamics, it is shown that this approach leads to quantum synchronization between both two‐level systems. Moreover, in this digital–analog block decomposition, the fundamental elements of a quantum machine learning protocol can be identified, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If the algorithm can be additionally equipped 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 experiences different loss/decoherence mechanisms, and gives the flexibility to choose the synchronization state. Finally, an implementation is proposed, based on current technologies in superconducting circuits.

Journal

Advanced Quantum TechnologiesWiley

Published: Aug 1, 2019

Keywords: ; ;

References