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arXiv:1709.08519 · Advanced Quantum Technologies

Enhanced Quantum Synchronization via Quantum Machine Learning

F. A. Cárdenas-López, Mikel Sanz, J. C. Retamal, E. Solano

Published Jan 7, 2019• 20 citations

Abstract

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.

DOI: 10.1002/qute.201800076

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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.

The cover figure represents a system comprising two atoms, which are called Agent and Environment, coupled to a central atom, called Register, in a dissipative reservoir. Additionally, a laser shines the Agent atom to counterbalance the losses. The machine learning protocol consists of measuring the Register and, according to the measurement outcome, act on the adjacent systems via classical communication. This protocol speeds up the quantum synchronization between Agent and Environment. For further information see article number 1800076 by Mikel Sanz and co-workers.

We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.

We propose a protocol to perform generalized quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits [L. Lamata, Sci. Rep. 7, 1609 (2017)], in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.

Machine learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the contrary, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault‐tolerant quantum computer. In this perspective, we review some recent works that employ this digital‐analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.

Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.