How useful can machine learning be in a quantum laboratory? Here we raise the
question of the potential of intelligent machines in the context of scientific
research. A major motivation for the present work is the unknown reachability
of various entanglement classes in quantum experiments. We investigate this
question by using the projective simulation model, a physics-oriented approach
to artificial intelligence. In our approach, the projective simulation system
is challenged to design complex photonic quantum experiments that produce
high-dimensional entangled multiphoton states, which are of high interest in
modern quantum experiments. 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.

本文探讨了机器学习在科学研究中的潜力，使用物理导向的人工智能方法研究了在量子实验中产生高维纠缠多光子态的复杂光子量子实验的设计与实现，自主学习创造了多种纠缠态并提高了实现效率，提出了机器在未来研究中具有更加创造性的可能。