Year: 2021
Type: article
Abstract: We show that combining randomized measurement protocols with importance sampling allows for characterizing entanglement in significantly larger quantum systems and in a more efficient way than in previous work. A drastic reduction of statistical errors is obtained using classical techniques of machine learning and tensor networks using partial information on the quantum state. In current experimental settings of engineered many-body quantum systems this significantly increases the (sub-)system sizes for which entanglement can be measured. In particular, we show an exponential reduction of the required number of measurements to estimate the purity of product states and GHZ states.
Source: Physical Review Letters
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Funders: Simons Foundation, European Commission, Agence Nationale de la Recherche, Austrian Science Fund, Studienstiftung des Deutschen Volkes, Deutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der Wissenschaften, QuantERA, Horizon 2020 Framework Programme, Air Force Office of Scientific Research
Awards: G168288183, G2897843612, G3065493051, G4345038503, Probing and verifying large-scale quantum technologies via randomized measurements, G4786631470, G4865054624, G5032364540, G5296751580, QuantERA ERA-NET Cofund in Quantum Technologies, G802755974, G8420134524, Programmable Atomic Large-Scale Quantum Simulation, G899235810
OpenAlex ID: W3134417373