Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits

Abstract

Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only continuous optimization variables, the representational power of the variational ansatz can sometimes be significantly enhanced by adding certain discrete optimization variables, as is exemplified by the generalized quantum approximate optimization algorithm (QAOA). However, the hybrid discretecontinuous optimization problem in the generalized QAOA poses a challenge to the optimization. We propose a new algorithm called MCTS-QAOA, which combines a Monte Carlo tree search method with an improved natural policy gradient solver to optimize the discrete and continuous variables in the quantum circuit, respectively. We find that MCTS-QAOA has excellent noiseresilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA.

Meta-Learning Digitized-Counterdiabatic Quantum Optimization

“…The incorporation of machine learning into quantum algorithms has been reported to enhance the performance, for instance, using RL algorithms [45,46] such as Q-learning [47,48], policy gradient [49][50][51] and Alphazero [52] in VQAs, policy gradient as an alternative optimizer for QAOA [53], the implementation of Q-learning to solve combinatorial problems [54], finding the groundstate of transverse Ising model [55] by proximal policy optimization (PPO), hybrid optimization by Monte Carlo search [56] etc.…”