  Quantum annealing is a practical approach to approximately implement the
adiabatic quantum computational model under a real-world setting. The goal of
an adiabatic algorithm is to prepare the ground state of a problem-encoded
Hamiltonian at the end of an annealing path. This is typically achieved by
driving the dynamical evolution of a quantum system slowly to enforce
adiabaticity. Properly optimized annealing schedules often significantly
accelerate the computational process. Inspired by the recent success of deep
reinforcement learning such as DeepMind's AlphaZero, we propose a Monte Carlo
Tree Search (MCTS) algorithm and its enhanced version boosted with neural
networks, which we name QuantumZero (QZero), to automate the design of
annealing schedules in a hybrid quantum-classical framework. Both the MCTS and
QZero algorithms perform remarkably well in discovering effective annealing
schedules even when the annealing time is short for the 3-SAT examples we
consider in this study. Furthermore, the flexibility of neural networks allows
us to apply transfer-learning techniques to boost QZero's performance. We
demonstrate in benchmark studies, that MCTS and QZero perform more efficiently
than other reinforcement learning algorithms in designing annealing schedules.
