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Updated: Jan 6, 2026

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提高基于样本的量子诊断的准确性和效率,使用无相辅助场量子蒙特卡洛

Don Danilov¹, Javier Robledo-Moreno², Kevin J Sung²

¹Department of Chemistry, Rice University, Houston, Texas 77005-1892, United States.

Journal of chemical theory and computation

|November 3, 2025

概括

这项研究将量子计算算法 (量子选择配置交互/基于样本的量子诊断) 与经典方法 (无相辅助场量子蒙特卡罗) 结合起来,以准确地解决复杂的分子问题,显著减少计算需求.

科学领域:

量子计算是一种量子计算.
计算化学是一种计算化学.
量子算法中的量子算法

背景情况:

量子选择配置交互 (QSCI) 和基于样本的量子诊断 (SQD) 是解决电子施罗丁格方程的有希望的量子算法.
这些方法通过准备量子电路和测量配置来利用杂的量子计算机来形成经典哈密尔顿对角化的子空间.

研究的目的:

研究一种混合量子-经典方法,将量子硬件的SQD试验波函数与无相辅助场量子蒙特卡洛 (ph-AFQMC) 结合起来.
评估这种混合方法在恢复分子解离问题的相关性能量方面的效率.

主要方法:

在量子硬件上使用QSCI/SQD协议来生成试验波函数.
使用一种非扰动性随机方法,ph-AFQMC,与截断的SQD试验波函数.
将该方法应用于N₂和 [2Fe - 2S] 集群模型的解离.

主要成果:

混合量子-经典方法成功地回收了大量的相关性能量 (O(100) mHa,用于研究的分子系统.
证明使用量子硬件的SQD试验波函数与ph-AFQMC显著降低了与纯 QSCI/SQD 相比的采样负担.

结论:

这种混合量子-经典组合为现有方法提供了令人信服的替代方案,可能减少对量子状态断层扫描的依赖.
这种方法对在近期量子设备上有效解决复杂的电子结构问题充满希望.

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