Multiqubit and multilevel quantum reinforcement learning with quantum technologies

Abstract

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.

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• Reinforcement learning for semi-autonomous approximate quantum eigensolver
• Retrieving Quantum Information with Active Learning
• Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
• High-dimensional reinforcement learning for optimization and control of ultracold quantum gases
• Quantum Measurement Classification with Qudits
• A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning
• Deep Reinforcement Learning with Quantum-inspired Experience Replay
• Implications of Deep Circuits in Improving Quality of Quantum Question Answering