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Variational quantum algorithms stand at the forefront of simulations onnear-term and future fault-tolerant quantum devices. While most variationalquantum algorithms involve only continuous optimization variables, therepresentational power of the variational ansatz can sometimes be significantlyenhanced by adding certain discrete optimization variables, as is exemplifiedby the generalized quantum approximate optimization algorithm (QAOA). However,the hybrid discrete-continuous optimization problem in the generalized QAOAposes a challenge to the optimization. We propose a new algorithm calledMCTS-QAOA, which combines a Monte Carlo tree search method with an improvednatural policy gradient solver to optimize the discrete and continuousvariables in the quantum circuit, respectively. We find that MCTS-QAOA hasexcellent noise-resilience properties and outperforms prior algorithms inchallenging instances of the generalized QAOA.

Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits