Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
This addresses optimization difficulties in variational quantum algorithms for near-term quantum computing, representing an incremental improvement in method.
The paper tackles the hybrid discrete-continuous optimization challenge in the generalized Quantum Approximate Optimization Algorithm (QAOA) by proposing MCTS-QAOA, which combines Monte Carlo tree search with an improved natural policy gradient solver, resulting in superior noise-resilience and outperforming prior algorithms on challenging instances.
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 discrete-continuous 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 noise-resilience properties and outperforms prior algorithms in challenging instances of the generalized QAOA.