Year: 2024
Type: article
Abstract: In quantum information theory, the accurate estimation of observables is pivotal for quantum information processing, playing a crucial role in computational and communication protocols. This work introduces a technique for estimating such objects, leveraging an underutilized resource in the inversion map of classical shadows that greatly refines the estimation cost of target observables without incurring any additional overhead. A generalized framework for computing and optimizing additional degrees of freedom in the homogeneous space of the shadow inversion is given that may be adapted to a variety of near-term problems. In the special case of local measurement strategies, we show feasible optimization leading to an exponential separation in sample complexity versus the standard approach and, in an exceptional case, we give nontrivial examples of optimized postprocessing for local measurements, achieving the same efficiency as the global Cliffords shadows. Published by the American Physical Society 2024
Source: Physical Review Research
Authors: Andrea Caprotti, Joshua Morris, Borivoje Dakić
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,625
Funder: Austrian Science Fund
Awards: G4157035715, G8590477180
OpenAlex ID: W4402795233