Learning Mixed Quantum States in Large-Scale Experiments
Year: 2026
Type: article
Abstract: We present and test a protocol to learn the matrix-product operator (MPO) representation of an experimentally prepared quantum state. The protocol takes as input classical shadows corresponding to local randomized measurements, and outputs the tensors of an MPO maximizing a suitably defined fidelity with the experimental state. The tensor optimization is carried out sequentially, similarly to the well-known density matrix renormalization group algorithm. Our approach is provably efficient under certain technical conditions expected to be met in short-range correlated states and in typical noisy experimental settings. Under the same conditions, we also provide an efficient scheme to estimate fidelities between the learned and the experimental states. We experimentally demonstrate our protocol by learning entangled quantum states of up to N=96 qubits in a superconducting quantum processor. Our method upgrades classical shadows to large-scale quantum computation and simulation experiments.
Source: Physical Review Letters
DOI: 10.1103/rbg2-f61m
Institutions: Centre National de la Recherche Scientifique, Laboratoire de Physique et Modélisation des Milieux Condensés, Université Grenoble Alpes, Institute of Science and Technology Austria, Munich Center for Quantum Science and Technology, Technical University of Munich, Institut Fourier, Max Planck Institute of Quantum Optics, Austrian Academy of Sciences, Universität Innsbruck, Institute for Quantum Optics and Quantum Information Innsbruck, Istituto Nazionale di Fisica Nucleare, Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, University of Bologna
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,750
Funders: European Commission, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Bundesministerium für Bildung und Forschung, Erwin Schrödinger International Institute for Mathematics and Physics, Horizon 2020 Framework Programme, HORIZON EUROPE Framework Programme, European Research Council
Awards: G1190173965, G1768993395, Programmable Atomic Large-scale Quantum Simulation 2 - SGA1, Etude de la pile quantique : Algorithmes, modèles de calcul et simulation pour l’informatique quantique, Initiative Nationale Hybride HPC Quantique – R&D et Support des communautés, Stochastic Quantum Evolutions, Qubits à Atomes Froids pour la Simulation et le Calcul Quantiques, G4710139657, Probing and verifying large-scale quantum technologies via randomized measurements, G5334593051, G6248895214, G6268741103, G8059206513, EXC 2111: Munich Center for Quantum Science and Technology (MCQST), G8727180634, G8959349008
OpenAlex ID: W4415143634