Abstract
We present an experimental realisation 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.
Highlights
IntroductionQuantum machine learning [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] is a field of research that has raised much attention in the past few years, especially for the expectation that it may enhance the machine learning calculations in current and future technology
We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer
We review the protocol of [1], which we subsequently implement in the Rigetti cloud quantum computer
Summary
Quantum machine learning [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] is a field of research that has raised much attention in the past few years, especially for the expectation that it may enhance the machine learning calculations in current and future technology. The machine learning field, inside artificial intelligence, is divided into three main areas: supervised learning, unsupervised learning and reinforcement learning [30]. The third one considers an intelligent agent that interacts with its outer world, the environment, gathering information from it, as well as acting on it, being employed, e.g., in robotics. Reinforcement learning can be considered as the most similar way in which human beings learn, via interactions with their outer world
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