Neural-network quantum state tomography

• Published in: Nature physics Vol. 14; no. 5; pp. 447 - 450
• Main Authors: Torlai, Giacomo, Mazzola, Guglielmo, Carrasquilla, Juan, Troyer, Matthias, Melko, Roger, Carleo, Giuseppe
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.05.2018; Nature Publishing Group
• Subjects: 119/118; 639/705/1042; 639/766/483/2802; 639/766/483/3924; 639/766/483/481; 639/766/930/12; Artificial intelligence; Atomic; Classical and Continuum Physics; Complex Systems; Computer simulation; Condensed Matter Physics; Entangled states; Letter; Machine learning; Mathematical and Computational Physics; Molecular; Neural networks; Optical and Plasma Physics; Physics; Physics and Astronomy; Quantum computers; Quantum entanglement; Quantum theory; Qubits (quantum computing); Theoretical; Tomography
• ISSN: 1745-2473; 1745-2481
• DOI: 10.1038/s41567-018-0048-5

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 to reconstruct the full quantum state from simple measurements, and therefore provides a key tool to obtain reliable analytics 1 – 3 . However, exact brute-force approaches to QST place a high demand on computational resources, making them unfeasible for anything except small systems 4 , 5 . Here we show how machine learning techniques can be used to perform QST of highly entangled states with more than a hundred qubits, to a high degree of accuracy. We demonstrate that machine learning 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 ultracold-atom quantum simulators 6 – 8 . Unsupervised machine learning techniques can efficiently perform quantum state tomography of large, highly entangled states with high accuracy, and allow the reconstruction of many-body quantities from simple experimentally accessible measurements.