Papers


: more details    : arXiv    : link to the journal


#information   #learning   #foundations   

#states   #channels   #CV systems   #qnn   

· display all papers ·
· show abstracts ·
· home ·


Optimal tomography of bosonic and fermionic Gaussian states  

Senrui Chen, Marco Fanizza, FG, Ludovico Lami, Francesco Anna Mele, Michael Walter, Freek Witteveen
arXiv:2607.11847


FOCS26 

Quantum channel tomography: optimal bounds and a Heisenberg-to-classical phase transition  

Kean Chen, FG, Aadil Oufkir, Nengkun Yu, Zhicheng Zhang
arXiv:2604.2604.17369


Improved Lower Bounds for Learning Quantum Channels in Diamond Distance  

Aadil Oufkir, FG
arXiv:2601.04180


Random Stinespring superchannel: converting channel queries into dilation isometry queries  

FG\(^\ast\), Francesco Anna Mele\(^\ast\), Haimeng Zhao\(^\ast\), Marco Fanizza\(^\dagger\), Ludovico Lami\(^\dagger\)
arXiv:2512.20599


QIP26 QCTiP26 

Is it Gaussian? Testing bosonic quantum states    

FG\(^\ast\), Freek Witteveen\(^\ast\), Francesco Anna Mele, Lennart Bittel, Salvatore F. E. Oliviero, David Gross, Michael Walter
arXiv:2510.07305


Quantitative convergence of trained quantum neural networks to a Gaussian process    

Anderson Melchor Hernandez, FG, Davide Pastorello, Giacomo De Palma
Ann. Henri Poincaré (2025)


TQC24 ICMP24 

Trained quantum neural networks are Gaussian processes      

FG, Giacomo De Palma
Communications in Mathematical Physics 406 (4), 1-146 (2025)