Armando Angrisani

According to our database, Armando Angrisani authored at least 12 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

When quantum resources backfire: Non-gaussianity and symplectic coherence in noisy bosonic circuits.

Varun Upreti, Ulysse Chabaud, Zoë Holmes, Armando Angrisani

CoRR, October, 2025

Quantum Differential Privacy in the Local Model.

Armando Angrisani, Elham Kashefi

IEEE Trans. Inf. Theory, May, 2025

Role of scrambling and noise in temporal information processing with quantum systems.

Weijie Xiong, Zoë Holmes, Armando Angrisani, Yudai Suzuki, Thiparat Chotibut, Supanut Thanasilp

CoRR, May, 2025

Efficient simulation of parametrized quantum circuits under non-unital noise through Pauli backpropagation.

Victor Martinez, Armando Angrisani, Ekaterina Pankovets, Omar Fawzi, Daniel Stilck França

CoRR, January, 2025

Learning unitaries with quantum statistical queries.

Armando Angrisani

Quantum, 2025

2024

Efficient quantum-enhanced classical simulation for patches of quantum landscapes.

Sacha Lerch, Ricard Puig-i-Valls, Manuel S. Rudolph, Armando Angrisani, Tyson Jones, Marco Cerezo, Supanut Thanasilp, Zoë Holmes

CoRR, 2024

Classically estimating observables of noiseless quantum circuits.

Armando Angrisani, Alexander Schmidhuber, Manuel S. Rudolph, Marco Cerezo, Zoë Holmes, Hsin-Yuan Huang

CoRR, 2024

2023

The disparate impact of noise on quantum learning algorithms. (L'impact disparate du bruit sur les algorithmes d'apprentissage quantique).

Armando Angrisani

PhD thesis, 2023

A unifying framework for differentially private quantum algorithms.

Armando Angrisani, Mina Doosti, Elham Kashefi

CoRR, 2023

2022

Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms.

Armando Angrisani, Mina Doosti, Elham Kashefi

CoRR, 2022

Quantum Local Differential Privacy and Quantum Statistical Query Model.

Armando Angrisani, Elham Kashefi

CoRR, 2022

2021

Probably approximately correct quantum source coding.

Armando Angrisani, Brian Coyle, Elham Kashefi

CoRR, 2021