2017/03/31 by Giacomo Torlai, Guglielmo Mazzola, Juan Carrasquilla +4 · 3 citations
Computer Science · Physics and Astronomy · #Advanced Thermodynamics and Statistical Mechanics #Cold Atom Physics and Bose-Einstein Condensates #Quantum Information and Cryptography #cond-mat.dis-nn #cond-mat.quant-gas #physics.comp-ph #quant-ph
paper · pdf · doi:10.1038/s41567-018-0048-5
published as Nature Physics 14, 447-450 (2018) · Update version and method, now discussing how to reconstruct the complex amplitudes of the wave function
arxiv created 2017/10/23 · openalex publication_date 2018/02/23 · crossref created 2018/02/23 · crossref issued 2018/02/26 · crossref published 2018/02/26 · crossref published-online 2018/02/26 · crossref published-print 2018/05/01 · arxiv updated 2018/05/17 · crossref deposited 2023/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23 · crossref indexed 2026/08/04
The experimental realization of increasingly complex synthetic quantum systems calls for the development of general theoretical methods, to validate and fully exploit quantum resources. Quantum-state tomography (QST) aims at reconstructing the full quantum state from simple measurements, and therefore provides a key tool to obtain reliable analytics. Brute-force approaches to QST, however, demand resources growing exponentially with the number of constituents, making it unfeasible except for small systems. Here we show that machine learning techniques can be efficiently used for QST of highly-entangled states, in both one and two dimensions. Remarkably, the resulting approach allows one to reconstruct traditionally challenging many-body quantities - such as the entanglement entropy - from simple, experimentally accessible measurements. This approach can benefit existing and future generations of devices ranging from quantum computers to ultra-cold atom quantum simulators.