Date: Thursday 27/9/2024

Time: 11:00 am

Place:  Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) 

Title: Entanglement detection using Quantum Support Vector Machines

Abstract: The quantum separability problem addresses the detection of quantum entanglement, a fundamental feature of quantum mechanics, meaning that we want to determine if a certain quantum state is entangled or separable. This problem has been shown to be NP-hard (1). Our starting point will be the works from Refs (2) and (3), in which the problem is taken as a binary classification task that is approached with classical machine learning models. In this work, we propose the application of quantum machine learning, in particular quantum support vector machines (QSVM), to the quantum separability problem. By studying and optimizing various encoding circuits, the quantum SVM shows comparable discrimination power to other traditional classical kernels.

(1) Gurvits L. Classical deterministic complexity of Edmonds’Problem and quantum entanglement

(2) Casalé B, Di Molfetta G, Anthoine S, Kadri H. Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens.

(3) Ureña J, Sojo A, Bermejo J, Manzano D. Entanglement detection with classical deep neural networks.

For those of you unable to attend the seminar, we have set-up the following  google meet link for the upcoming seminar.

We would also like to bring to your attention the groups seminar page 

 

 

where you can find relevant material and information about past, present, and future seminars. 

See you all there

DJM.