Work

Classical optimization of reduced density matrix estimations with classical shadows using N -representability conditions under shot noise considerations

Year: 2026

Type: article

Abstract: Classical shadow tomography has become a powerful tool in learning about quantum states prepared on a quantum computer. Recent works have used classical shadows to variationally enforce N -representability conditions on the two-particle reduced density matrix. In this paper, we build upon previous research by choosing an improved estimator within classical shadow tomography and rephrasing the optimization constraints, resulting in an overall enhancement in performance under comparable measurement shot budgets. We further explore the specific regimes where these methods outperform the unbiased estimator of the stand-alone classical shadow protocol and quantify the potential savings in numerical studies.

Source: Physical Review Research

DOI: 10.1103/hwcl-yfy4

Authors: Gian-Luca R. Anselmetti, Matthias Degroote, Nikolaj Moll, Raffaele Santagati, Michael Streif

Institution: Boehringer Ingelheim (Germany)

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

OpenAlex ID: W4404995250

FWCI:

Cites: 45

Cited by: 1

Related to: 10