Gray, J;
Banchi, L;
Bayat, A;
Bose, S;
(2018)
Machine-Learning-Assisted Many-Body Entanglement Measurement.
Physical Review Letters
, 121
, Article 150503. 10.1103/PhysRevLett.121.150503.
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Abstract
Entanglement not only plays a crucial role in quantum technologies, but is key to our understanding of quantum correlations in many-body systems. However, in an experiment, the only way of measuring entanglement in a generic mixed state is through reconstructive quantum tomography, requiring an exponential number of measurements in the system size. Here, we propose a machine-learning-assisted scheme to measure the entanglement between arbitrary subsystems of size NA and NB, with OðNA þ NBÞ measurements, and without any prior knowledge of the state. The method exploits a neural network to learn the unknown, nonlinear function relating certain measurable moments and the logarithmic negativity. Our procedure will allow entanglement measurements in a wide variety of systems, including strongly interacting many-body systems in both equilibrium and nonequilibrium regimes.
| Type: | Article |
|---|---|
| Title: | Machine-Learning-Assisted Many-Body Entanglement Measurement |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1103/PhysRevLett.121.150503 |
| Publisher version: | https://doi.org/10.1103/PhysRevLett.121.150503 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10061878 |
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