Multiqubit and multilevel quantum reinforcement learning with quantum technologies
This work addresses the problem of enhancing quantum control and machine learning efficiency for researchers in quantum technologies, though it appears incremental by building on existing quantum reinforcement learning methods.
The authors tackled the challenge of implementing quantum reinforcement learning without requiring coherent feedback, enabling broader applicability across various quantum systems. They proposed a protocol using multiqubit and multilevel systems, with potential implementations in trapped ions and superconducting circuits.
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.