Description
Entanglement is one of the most fundamental features of quantum mechanics, distinguishing it from classical physics. However, determining whether a quantum state is separable or entangled is an NP-hard problem, posing significant challenges for classical and quantum computation. In this talk, we will review recent advances in autonomous methods for entanglement detection, including classical neural networks, support vector machines, and variational quantum algorithms. We will discuss their strengths, limitations, and potential applications in quantum information science.
Primary author
Prof.
Daniel Manzano Diosdado
(Universidad de Granada)
