A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
2021
PRX Quantum
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
doi:10.1103/prxquantum.2.040342
fatcat:aaoxckkfwzb4rpwwmtdbxs6hpm