Measuring Quantum Entanglement from Local Information by Machine
Learning
- URL: http://arxiv.org/abs/2209.08501v1
- Date: Sun, 18 Sep 2022 08:15:49 GMT
- Title: Measuring Quantum Entanglement from Local Information by Machine
Learning
- Authors: Yulei Huang, Liangyu Che, Chao Wei, Feng Xu, Xinfang Nie, Jun Li,
Dawei Lu, and Tao Xin
- Abstract summary: Entanglement is a key property in the development of quantum technologies.
We present a neural network-assisted protocol for measuring entanglement in equilibrium and non-equilibrium states of local Hamiltonians.
- Score: 10.161394383081145
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Entanglement is a key property in the development of quantum technologies and
in the study of quantum many-body simulations. However, entanglement
measurement typically requires quantum full-state tomography (FST). Here we
present a neural network-assisted protocol for measuring entanglement in
equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST,
it can learn comprehensive entanglement quantities from single-qubit or
two-qubit Pauli measurements, such as R\'enyi entropy, partially-transposed
(PT) moments, and coherence. It is also exciting that our neural network is
able to learn the future entanglement dynamics using only single-qubit traces
from the previous time. In addition, we perform experiments using a nuclear
spin quantum processor and train an adoptive neural network to study
entanglement in the ground and dynamical states of a one-dimensional spin
chain. Quantum phase transitions (QPT) are revealed by measuring static
entanglement in ground states, and the entanglement dynamics beyond measurement
time is accurately estimated in dynamical states. These precise results
validate our neural network. Our work will have a wide range of applications in
quantum many-body systems, from quantum phase transitions to intriguing
non-equilibrium phenomena such as quantum thermalization.
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