DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
Fradkin, Jordan-wigner transformation for quantum- spin systems in two dimensions and fractional statistics, Physical review letters63, 322 (1989)
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Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.