蒙特卡洛投影量子本征求解器的高效测量方案
Efficient measurement schemes for the Monte Carlo projective quantum eigensolver
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中文总结 AI 辅助
本研究将VQE的联合测量降开销技术扩展至MC-PQE，经12量子比特内分子系统测试，该方案可使MC-PQE测量标准误差降5-10倍，性能优于经典阴影层析技术。
中文摘要 AI 辅助
蒙特卡洛投影量子本征求解器（MC-PQE）作为变分量子本征求解器（VQE）等混合算法的替代方案被提出，其采用受量子蒙特卡洛启发的能量估计方案，相较于同类传统方法降低了所需的测量成本。然而针对VQE，已开发出大量通过联合测量多个可观测量来减少测量开销的技术。本研究将这些方法扩展至MC-PQE所需的非对称期望值，并针对最多包含12个量子比特的多种分子系统评估不同技术的性能。结果表明，完全对易的泡利项分组结合定制化测量分配技术，可在相同量子测量总数下将标准误差降低5-10倍；对于所考虑的系统，传统哈密顿量分组测量优于基于可处理经典阴影层析的技术。
英文摘要
The Monte Carlo Projective Quantum Eigensolver (MC-PQE) was recently introduced as an alternative to hybrid algorithms like the variational quantum eigensolver (VQE). By using a quantum Monte Carlo-inspired scheme for energy estimation, MC\nobreakdash-PQE was found to decrease the required measurement cost relative to comparable conventional approaches. However, in the context of VQE, numerous techniques have been developed to reduce the measurement overhead by joint measurement of multiple observables. In this work, we extend these approaches to the asymmetric expectation values required in MC-PQE and assess the performance of different techniques for various molecular systems of up to 12 qubits. We find full commuting Pauli term grouping combined with tailored measurement allocation techniques leads to a 5-10$\times$ reduction in standard error for the same total number of quantum measurements. Conventional Hamiltonian grouped measurement outperforms tractable classical shadows tomography based techniques for the considered systems.
