AI 中文总结
该研究针对准概率分解采样开销过高问题，提出重加权策略复用样本，在保留态编码ansatz结构的同时降低开销，经实验验证其在含噪硬件下优于其他估计技术。
AI 中文摘要
近期量子设备受限于噪声与硬件约束，推动了以增加采样开销为代价换取电路复杂度降低的算法方法。例如准概率分解（QPDs）可通过多个含局域操作的电路替代非局域操作，但相关采样开销通常呈指数级缩放，限制了其实用性。本研究针对参数设置间具有相同变分结构的电路，为QPDs引入重加权策略，通过复用样本降低采样开销。我们首先以估计参数化量子态间的保真度为例验证该方法，保真度是变分时间演化和量子核方法中的关键基本操作，重要的是，该设置可在保留态编码ansatz结构的同时控制指数级QPD采样开销。随后我们将该方法应用于通过同时扰动随机近似估计量子几何张量的实部，发现存在实际硬件噪声时，本方法优于其他标准估计技术。这些结果凸显了重加权策略在扩展QPD方法在变分量子算法中适用性的潜力。
英文摘要
Near-term quantum devices are limited by noise and hardware constraints, motivating algorithmic approaches that trade circuit complexity for increased sampling overhead. Quasi-probability decompositions (QPDs), for example, allow replacing non-local operations by multiple circuits with local operations, but the associated sampling overhead generally scales exponentially and limits their practicality. In this work, we introduce a reweighting strategy for QPDs for circuits with the same variational structure across parameter settings, reusing samples and thereby reducing the sampling overhead. We first demonstrate this approach by estimating fidelities between parameterized quantum states, a key primitive in variational time evolution and quantum kernel methods. Importantly, this setup allows controlling the exponential QPD sampling overhead while preserving the structure of the state-encoding ansatz. We then apply the method to estimate the real part of the quantum geometric tensor using the simultaneous perturbation stochastic approximation and find that, in the presence of realistic hardware noise, our method outperforms other standard estimation techniques. These results highlight the potential of reweighting strategies to extend the applicability of QPD-based methods in variational quantum algorithms.
Comments10 pages, 7 figures, 2026 IEEE International Conference on Quantum Computing and Engineering (QCE)
