Optimizing quantum annealing schedules with Monte Carlo tree search enhanced by MindSpore

One of the key research focuses in quantum annealing is the design and optimization of annealing schedules to enhance computational efficiency, enabling large-scale applications. QuantumZero (QZero) pioneered the integration of Monte Carlo Tree Search (MCTS) with neural networks to autonomously design annealing schedules within a hybrid quantum-classical framework. This approach is distinguished by its ability to enhance the performance of Monte Carlo Tree Search through the integration of neural networks, enabling the efficient design of annealing paths even with limited annealing time. The paper presents an optimized QZero method based on intuitive reasoning theory and MindSpore, which further enhances QZero’s ability to conserve computational resources and resist noise. In terms of learning efficiency, the optimized QZero algorithm improves the convergence speed of the neural network by 93% compared to the original algorithm. Notably, the average number of quantum annealing queries required to achieve 99% fidelity is reduced by 45.09%. Regarding noise resistance, the optimized QZero algorithm requires 34.27% fewer quantum annealing queries to reach 99% fidelity compared to the original algorithm. The optimized QZero algorithm demonstrates strong competitiveness in optimizing quantum annealing schedules.