标题： 量子硬件在分子能量估算中使用变分量子本征求解器的局限性 显示英文标题

标题： Limitations of Quantum Hardware for Molecular Energy Estimation Using VQE

摘要： 变分量子本征求解器（VQE）是量子化学中解决电子结构问题的最有前景的量子算法之一，尤其是在嘈杂的中等规模量子（NISQ）时代。 在这项研究中，我们调查了当前量子硬件上实现的VQE算法的能力和局限性，用于确定分子基态能量，重点关注自适应导数组装伪 Trotter 近似 VQE（ADAPT-VQE）。 为了应对由分子哈密顿量带来的重大计算挑战，我们探索了各种简化哈密顿量、优化近似和通过修改COBYLA优化器改进经典参数优化的策略。 这些增强功能被集成到一个定制的量子计算实现中，旨在最小化电路深度和计算成本。 使用苯作为基准系统，我们在IBM量子计算机上展示了这些优化的应用。 尽管进行了这些改进，我们的结果显示了当前量子硬件施加的限制，特别是量子噪声对状态准备和能量测量的影响。 当今设备中的噪声水平阻碍了对分子哈密顿量进行足够准确的评估，从而无法产生可靠的量子化学见解。 最后，我们推断了未来量子硬件的需求，以实现使用VQE算法进行实用且可扩展的量子化学计算。 这项工作为推进量子算法和硬件以实现在分子建模中的量子优势提供了路线图。 显示英文摘要

摘要： Variational quantum eigensolvers (VQEs) are among the most promising quantum algorithms for solving electronic structure problems in quantum chemistry, particularly during the Noisy Intermediate-Scale Quantum (NISQ) era. In this study, we investigate the capabilities and limitations of VQE algorithms implemented on current quantum hardware for determining molecular ground-state energies, focusing on the adaptive derivative-assembled pseudo-Trotter ansatz VQE (ADAPT-VQE). To address the significant computational challenges posed by molecular Hamiltonians, we explore various strategies to simplify the Hamiltonian, optimize the ansatz, and improve classical parameter optimization through modifications of the COBYLA optimizer. These enhancements are integrated into a tailored quantum computing implementation designed to minimize the circuit depth and computational cost. Using benzene as a benchmark system, we demonstrate the application of these optimizations on an IBM quantum computer. Despite these improvements, our results highlight the limitations imposed by current quantum hardware, particularly the impact of quantum noise on state preparation and energy measurement. The noise levels in today's devices prevent meaningful evaluations of molecular Hamiltonians with sufficient accuracy to produce reliable quantum chemical insights. Finally, we extrapolate the requirements for future quantum hardware to enable practical and scalable quantum chemistry calculations using VQE algorithms. This work provides a roadmap for advancing quantum algorithms and hardware toward achieving quantum advantage in molecular modeling.