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.
Highlights
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that has attracted increasing attention in the last years
At variance with a previous result [23], we extend the realm of the quantum reinforcement learning protocol to multi-qubit, multi-level, and open quantum systems, permitting a wider set of scenarios
We study quantum reinforcement learning in the case where agent, environment and register are composed of qudits
Summary
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that has attracted increasing attention in the last years. Quantum harmonic oscillators, electronic energy levels in an ion, and superconducting artificial atoms such as transmons [47], where for some regimes of Josephson energy they must be considered as a three-level system In this context, it is interesting to extend the quantum reinforcement learning protocol developed here for cases where multilevel systems compound the agent, environment, and register. Considering our proposed protocol for single-qubit cases, we show that when we take into account multilevel systems, the number of interactions to obtain maximal learning fidelity is fixed and depends only on the number of agent subsystems in the protocol Let us illustrate this with an example of multilevel agent-environment-register state,
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