Machine Learning Assisted Many-Body Entanglement Measurement

Understand

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 $N_A$ and $N_B$, with $\mathcal{O}(N_A + N_B)$ measurements, and without any prior knowledge of the state.
• The method exploits a neural network to learn the unknown, non-linear function relating certain measurable moments and the logarithmic negativity.