We present an autonomous learning model which learns to design such complex experiments, without relying on previous knowledge or often flawed intuition. Our system not only learns how to design desired experiments more efficiently than the best previous approaches, but in the process also discovers nontrivial experimental techniques. Our work demonstrates that learning machines can offer dramatic advances in how experiments are generated.
The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments—a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.
The artificial agent develops new experiments by virtually placing mirrors, prisms or beam splitters on a virtual lab table. If its actions lead to a meaningful result, the agent has a higher chance of finding a similar sequence of actions in the future. This is known as a reinforcement learning strategy.
Read more at: https://phys.org/news/2018-01-artificial-agent-quantum.html#jCp