Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure

Résumé

We here extend Blind (i.e. unsupervised) Quantum Source Separation and Process Tomography methods. Considering disentanglement-based approaches, we introduce associated optimization algorithms which are much faster than the previous ones, since they reduce the number of source quantum state preparations required for adaptation by a factor of 10(3) typically. This is achieved by unveiling the parametric forms of the optimized cost functions, which allows us to derive a closed-form solution for their optimum.
Fichier non déposé

Dates et versions

hal-01694475 , version 1 (27-01-2018)

Identifiants

Citer

Yannick Deville, Alain Deville. Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure. LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION (LVA/ICA 2017), 2017, Unknown, Unknown Region. pp.438-448, ⟨10.1007/978-3-319-53547-0_41⟩. ⟨hal-01694475⟩
108 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More