activity
20202026
most citedHow to enhance quantum generative adversarial learning of noisy information

14 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2026

Quantum Gaussian processes for prediction of channel observations

Jonas Jäger, Yaroslav Khmelnitskiy, Paolo Braccia +4

Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited numb…

quant-ph2026

Provable and scalable quantum Gaussian processes for quantum learning

Jonas Jäger, Paolo Braccia, Pablo Bermejo +3

Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and we still lack learning framewor…

quant-ph2026

The commutant of fermionic Gaussian unitaries

Paolo Braccia, N. L. Diaz, Martin Larocca +2

In this work, we characterize the -th order commutants of fermionic Gaussian unitaries and of their particle-preserving subgroup acting on fermionic modes. These commutants…

quant-ph2021★ 1 cited

Quantum Noise Sensing by generating Fake Noise

Paolo Braccia, Leonardo Banchi, Filippo Caruso

Noisy-Intermediate-Scale-Quantum (NISQ) devices are nowadays starting to become available to the final user, hence potentially allowing to show the quantum speedups predicted by th…

quant-ph2020★ 14 cited

How to enhance quantum generative adversarial learning of noisy information

Paolo Braccia, Filippo Caruso, Leonardo Banchi

Quantum Machine Learning is where nowadays machine learning meets quantum information science. In order to implement this new paradigm for novel quantum technologies, we still need…