AI 中文总结
本研究以DMRG算法结合大型张量网络，生成含1400个粒子的LMG模型基态能量数据集，与VQE、SQD在IBM Eagle量子计算机上对比，得出子空间方法在NISQ时代性能更优的结论。
AI 中文摘要
随着量子计算的成熟，将其解决现实世界问题的性能与张量网络等有竞争力的经典方法进行基准测试至关重要。本研究利用密度矩阵重整化群（DMRG）算法计算Lipkin-Meshkov-Glick（LMG）模型的基态能量，作为与变分量子本征求解器（VQE）、基于样本的量子对角化（SQD）等流行的噪声中等规模量子（NISQ）算法对比的基准。通过在NERSC Perlmutter超级计算机上运行DMRG，我们提供了文献中最大的LMG基态能量数据集之一，包含最多1400个粒子系统的精确基态能量。我们将这些结果与IBM Eagle量子计算机上实现的VQE和SQD进行对比：VQE在6个粒子时结果误差在1%以内，其余所有粒子数均超过该阈值；而SQD将该范围扩展至17个粒子，表明在噪声中等规模量子时代，基于子空间的方法可能在精度、电路深度和噪声鲁棒性之间取得最佳平衡。
英文摘要
As quantum computing matures, it is critical to benchmark its real-world problem solving performance against competitive classical methods, such as tensor networks. In this work, we leverage the Density Matrix Renormalization Group (DMRG) algorithm to compute ground state energies of the Lipkin Meshkov Glick (LMG) model as a comparative benchmark against popular noisy intermediate-scale (NISQ) algorithms like the Variational Quantum Eigensolver (VQE) and Sample-Based Quantum Diagonalization (SQD) method. By running DMRG on the NERSC Perlmutter supercomputer, we provide one of the largest LMG ground state energy datasets in literature, containing accurate ground state energies for systems up to 1400 particles. We compare these results with VQE and SQD implementations on an IBM Eagle quantum computer for comparison. VQE achieved results within 1 percent error for 6 particles, while exceeding that threshold for all other values while SQD extended that range to 17 particles, suggesting that in a noisy intermediate scale quantum era, subspace-based approaches may strike the best balance between accuracy, circuit depth, and noise resilience.
Comments22 pages, 20 figures
