Quantum Adiabatic Algorithm Design using Reinforcement Learning

Understand

Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices.

• Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design.
• Our approach is generically applicable to a class of problems with solution hard-to-find but easy-to-verify, e.g., searching and NP-complete problems.
• We benchmark this approach in Grover-search and 3-SAT problems, and find that the adiabatic-algorithm obtained by our RL approach leads to significant improvement in the resultant success probability.