Auxiliary-Free Replica Shadows: Efficient Estimation of Multiple Nonlinear Quantum Properties
Abstract
Efficient estimation of nonlinear properties is a significant yet challenging task from quantum information processing to many-body physics. Current methodologies often suffer from an exponential sampling cost or require auxiliary qubits and deep quantum circuits. To address these limitations, we propose an efficient auxiliary-free replica shadow (AFRS) framework, which leverages the power of the joint entangling operation on a few input replicas while integrating the mindset of shadow estimation. We rigorously prove that AFRS can offer exponential improvements in estimation accuracy compared with the conventional shadow method, and facilitate the simultaneous estimation of various nonlinear properties, unlike the destructive swap test. Additionally, we introduce an advanced local-AFRS variant tailored to estimating local observables with constant-depth quantum circuits, significantly simplifying the experimental implementation. Our work paves the way for efficient and practical estimation of nonlinear properties on near-term quantum devices.
Cite
@article{arxiv.2407.20865,
title = {Auxiliary-Free Replica Shadows: Efficient Estimation of Multiple Nonlinear Quantum Properties},
author = {Qing Liu and Zihao Li and Xiao Yuan and Huangjun Zhu and You Zhou},
journal= {arXiv preprint arXiv:2407.20865},
year = {2025}
}
Comments
Main(10 pages, 5 figures), Appendix(27 pages, 9 figures), and comments are welcome. Major updates in v2 include: computational cost of classical post-processings, sample complexity scaling, circuit constructiin for t=3, additional practical applications (analytical and numerical results)