A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
Tekijät: García-Pérez Guillermo, Rossi Matteo AC, Sokolov Boris, Tacchino Francesco, Barkoutsos Panagiotis K, Mazzola Guglielmo, Tavernelli Ivano, Maniscalco Sabrina
Kustantaja: AMER PHYSICAL SOC
Julkaisuvuosi: 2021
Journal: PRX Quantum
Tietokannassa oleva lehden nimi: PRX QUANTUM
Lehden akronyymi: PRX QUANTUM
Artikkelin numero: ARTN 040342
Vuosikerta: 2
Numero: 4
Sivujen määrä: 17
DOI: https://doi.org/10.1103/PRXQuantum.2.040342
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/68194224
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
