论文标题
通过特定于问题的参数化量子电路来增强VQE收敛以优化问题
Enhancing VQE Convergence for Optimization Problems with Problem-specific Parameterized Quantum Circuits
论文作者
论文摘要
变异量子本素（VQE）算法因其在近期量子设备中的潜在使用而引起了兴趣。在VQE算法中，使用参数化的量子电路（PQC）来制备量子状态，然后将其用于计算给定的汉密尔顿的期望值。设计有效的PQC对于提高收敛速度至关重要。在这项研究中，我们通过动态生成包含问题约束的PQC来介绍针对优化问题量身定制的问题的PQC。这种方法通过专注于有益于VQE算法并加速收敛的统一转换来减少搜索空间。我们的实验结果表明，我们提出的PQC的收敛速度优于最先进的PQC，突出了特定问题的PQC在优化问题中的潜力。
The Variational Quantum Eigensolver (VQE) algorithm is gaining interest for its potential use in near-term quantum devices. In the VQE algorithm, parameterized quantum circuits (PQCs) are employed to prepare quantum states, which are then utilized to compute the expectation value of a given Hamiltonian. Designing efficient PQCs is crucial for improving convergence speed. In this study, we introduce problem-specific PQCs tailored for optimization problems by dynamically generating PQCs that incorporate problem constraints. This approach reduces a search space by focusing on unitary transformations that benefit the VQE algorithm, and accelerate convergence. Our experimental results demonstrate that the convergence speed of our proposed PQCs outperforms state-of-the-art PQCs, highlighting the potential of problem-specific PQCs in optimization problems.
