Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
This work advances quantum reinforcement learning for potential applications in quantum computing and AI, though it is incremental as it builds on existing theoretical proposals.
The researchers experimentally implemented a measurement-based adaptation protocol with quantum reinforcement learning on a Rigetti quantum computer, successfully reproducing the theoretical proposal and demonstrating initial progress toward a semiautonomous quantum agent.
We present an experimental realization of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits.