NEURAL NETWORK AGENT PLAYING SPIN HAMILTONIAN GAMES ON A QUANTUM COMPUTER

We develop an autonomous agent effectively interacting with noisy quantum computer to solve magnetism problems. By using the reinforcement learning the agent is trained to find the best-possible approximation of a spin Hamiltonian ground state from self-conducted ex-periments on quantum devices.