发表机构
University of Copenhagen; Technical University of Denmark; University of Southampton; University of Southern Denmark; Qedma Quantum Computing（哥本哈根大学; 丹麦技术大学; 南安普顿大学; 南丹麦大学; Qedma量子计算）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在IBM量子处理器上应用QESEM无偏误差缓解方法，成功计算了水分子的势能面，显著提升了能量精度，并揭示了采样开销作为关键瓶颈。
AI 中文摘要
量子误差缓解（QEM）对于从近期量子硬件中提取化学精度结果至关重要。许多广泛使用的QEM方法依赖于不受控的启发式方法，其偏差取决于特定的电路和噪声实现。在本工作中，我们在IBM的Aachen量子处理器上采用QESEM——一种基于表征的无偏准概率缓解方法——来计算对称拉伸水分子的基态势能面（PES）。我们考虑了一个经典优化的单层完美配对平铺酉乘积态拟设。我们将该拟设映射到对应于（4,4）活性空间和STO-3G基组的8量子比特寄存器。与态矢量参考相比，我们发现原始QPU结果在扫描的几何构型上通常高估基态能量约500 mHa。另一方面，QESEM缓解后的结果根据目标精度，大约落在100 mHa、30 mHa或“化学精度”（约1.5 mHa）以内。我们在宽松（0.1 Ha）和严格（0.01 Ha）精度目标下对QESEM进行基准测试，评估了单独和合并的运行批次。随着精度目标的收紧，准确性系统性提高，达到或超过文献中报道的结果。我们进一步量化了这些结果的采样成本，报告了每个精度级别所需的射击次数。我们的结果表明，在当前硬件误差水平下，达到最高精度需要大量的QPU时间。结果证明，QESEM提供的基于表征的无偏误差缓解能够在当前量子硬件上测量定量有意义的势能面。同时，它们突出了与QEM相关的采样开销，这仍然是通往更大化学问题和更高精度的核心瓶颈。
英文摘要
Quantum error mitigation (QEM) is essential for extracting chemically accurate results from near-term quantum hardware. Many widely used QEM methods rely on uncontrolled heuristics whose bias depends on the specific circuit and noise realization. In this work, we employ QESEM---a characterization-based, unbiased quasi-probabilistic mitigation method---on IBM's Aachen quantum processor to compute the ground-state potential energy surface (PES) of the symmetrically-stretched water molecule. We consider a classically-optimized, single-layer perfect-pairing tiled unitary product state ansatz. We map this ansatz to an 8-qubit register corresponding to a (4,4) active space and the STO-3G basis set. Compared to the statevector reference, we find that raw QPU results typically overestimate the ground-state energy by around 500~mHa across the scanned geometries. On the other hand, QESEM-mitigated results fall approximately within 100~mHa, 30~mHa, or ``chemical accuracy'' ($\sim$1.5~mHa), depending on the target precision. We benchmark QESEM at both loose (0.1~Ha) and tight (0.01~Ha) precision targets, evaluating both individual and merged batches of runs. As the precision target is tightened, the accuracy improves systematically, matching or exceeding results reported in the literature. We further quantify the sampling cost of these results, reporting the number of shots required at each precision level. Our results show how, with the current levels of hardware error, reaching the highest accuracies demands substantial QPU time. The results demonstrate that the characterization-based, unbiased error mitigation provided by QESEM allows to measure quantitatively meaningful potential energy surfaces on current quantum hardware. Concurrently, they highlight the sampling overhead associated with QEM, which remains a central bottleneck en route to larger chemical problems and higher precision.
Comments18 pages, 10 figures
