Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
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DOI：
10.1103/prxquantum.2.040342
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发表时间：
2021-11-29
期刊：
影响因子：
9.7
通讯作者：
Maniscalco, Sabrina
中科院分区：
文献类型：
--
作者：
Garcia-Perez, Guillermo;Rossi, Matteo A. C.;Maniscalco, Sabrina
许多在化学和材料科学等领域应用的著名量子计算算法都需要大量测量，这是未来现实世界用例的重要障碍。我们引入了一种新方法，通过自适应测量方案来解决这个问题。我们提出了一种算法，可以动态优化信息完整的正算子值测量（POVM），以便最大限度地减少相关成本函数估计中的统计波动。我们通过广泛的数值模拟提高了变分量子本征求解器计算分子哈密顿量基态能量的效率，展示了其优势。我们的结果表明，所提出的方法在效率方面与最先进的测量减少方法具有竞争力。此外，该方法的信息完整性提供了一个至关重要的优势，因为测量数据可以重复使用来推断其他感兴趣的数量。我们通过重复使用基态能量估计数据来执行高保真还原态断层扫描来证明这一前景的可行性。
Many prominent quantum computing algorithms with applications in fields such as chemistry and materials science require a large number of measurements, which represents an important roadblock for future real-world use cases. We introduce a novel approach to tackle this problem through an adaptive measurement scheme. We present an algorithm that optimizes informationally complete positive operator-valued measurements (POVMs) on the fly in order to minimize the statistical fluctuations in the estimation of relevant cost functions. We show its advantage by improving the efficiency of the variational quantum eigensolver in calculating ground-state energies of molecular Hamiltonians with extensive numerical simulations. Our results indicate that the proposed method is competitive with state-of-the-art measurement-reduction approaches in terms of efficiency. In addition, the informational completeness of the approach offers a crucial advantage, as the measurement data can be reused to infer other quantities of interest. We demonstrate the feasibility of this prospect by reusing ground-state energy-estimation data to perform high-fidelity reduced state tomography.