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

2024/11/27 by Gian-Luca Anselmetti, Anselmetti, Gian-Luca R., Matthias Degroote +7 · 1 citation
Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Blind Source Separation Techniques #Direction-of-Arrival Estimation Techniques

paper · pdf · doi:10.48550/arxiv.2411.18430

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 2-particle reduced density matrix. In this paper, we build upon previous research by choice of an improved estimator within classical shadow tomography and rephrasing the optimisation 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 standalone classical shadow protocol and quantify the potential savings in numerical studies.

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• Obtaining Accurate Ground-State Properties on Near-term Quantum Devices

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