Quantum Agents for Algorithmic Discovery
This work addresses the challenge of automating quantum algorithm design for researchers in quantum computing, though it is incremental as it rediscovers known algorithms.
The authors tackled the problem of discovering quantum algorithms autonomously by training quantum agents with reinforcement learning, resulting in the agents rediscovering key algorithms like the Quantum Fourier Transform and Grover's search without prior knowledge.
We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our agents learn: efficient logarithmic-depth quantum circuits for the Quantum Fourier Transform; Grover's search algorithm; optimal cheating strategies for strong coin flipping; and optimal winning strategies for the CHSH and other nonlocal games. The agents achieve these results directly through interaction, without prior access to known optimal solutions. This demonstrates the potential of quantum intelligence as a tool for algorithmic discovery, opening the way for the automated design of novel quantum algorithms and protocols.