Mario Gattobigio
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Mario Gattobigio has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Alejandro Kievsky
INFN-Pisa
2 shared publications - Paolo Recchia
Centre for Quantum Technologies, Centre for Quantum Technologies, Singapore
2 shared publications - Stéphane Bressan
National University of Singapore
2 shared publications Christian Miniatura
Centre for Quantum Technologies National University of Singapore Sin- gapore
1 shared publication- Christian Miniatura
Université Côte d'Azur
1 shared publication - Dario Poletti
Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore
1 shared publication
Latest Publications
- 29 May 2026
- Published by Springer Nature in Few-Body Systems
- Vol. 67 (2) , 37
Advancing Machine Learning Applications in Quantum Few-Body Systems
- 29 May 2026
- Published by Springer Nature in Few-Body Systems
- Vol. 67 (2) , 37
Advancing Machine Learning Applications in Quantum Few-Body Systems
- 19 November 2024
- Published by Springer Nature in Few-Body Systems
- Vol. 65 (4) , 1-26
The Steepest Slope toward a Quantum Few-Body Solution: Gradient Variational Methods for the Quantum Few-Body Problem
- 19 November 2024
- Published by Springer Nature in Few-Body Systems
- Vol. 65 (4) , 1-26
The Steepest Slope toward a Quantum Few-Body Solution: Gradient Variational Methods for the Quantum Few-Body Problem
- Open Access
- 6 February 2024
- Published by Sissa Medialab Srl
- Vol. 413, 016
The three-nucleon interaction in pionful and pionless effective field theory
After a brief historical perspective on the three-nucleon ($3N$) interaction, we will focus on the chiral $3N$ forces and discuss some of the main present discrepancies with experimental data on low-energy $p-d$ scattering. By including the subleading $3N$ contact interactions, expected to arise at the fifth order of the chiral expansion (N4LO), these discrepancies can be solved. However, part of the needed contributions can be regarded as fourth-order (N3LO), because they are induced by a unitary transformation devised to absorb redundant $NN$ contact interactions at N3LO. As a result, a satisfactory description in $NN$ and $3N$ systems can be achieved already at N3LO. Finally, the pionless case will be described, with particular emphasis on the different treatment of the $3N$ force, and on possible consequences for the chiral series.- 6 February 2024
- Published by Sissa Medialab Srl
- Vol. 413, 016
Open AccessThe three-nucleon interaction in pionful and pionless effective field theory
After a brief historical perspective on the three-nucleon ($3N$) interaction, we will focus on the chiral $3N$ forces and discuss some of the main present discrepancies with experimental data on low-energy $p-d$ scattering. By including the subleading $3N$ contact interactions, expected to arise at the fifth order of the chiral expansion (N4LO), these discrepancies can be solved. However, part of the needed contributions can be regarded as fourth-order (N3LO), because they are induced by a unitary transformation devised to absorb redundant $NN$ contact interactions at N3LO. As a result, a satisfactory description in $NN$ and $3N$ systems can be achieved already at N3LO. Finally, the pionless case will be described, with particular emphasis on the different treatment of the $3N$ force, and on possible consequences for the chiral series. - 9 December 2021
- Published by Springer Nature in Few-Body Systems
- Vol. 63 (1) , 1-10
Gaussian Parametrization of Efimov Levels: Remnants of Discrete Scale Invariance
- 9 December 2021
- Published by Springer Nature in Few-Body Systems
- Vol. 63 (1) , 1-10
Gaussian Parametrization of Efimov Levels: Remnants of Discrete Scale Invariance
- Open Access
Finding Quantum Critical Points with Neural-Network Quantum States
Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.
Open AccessFinding Quantum Critical Points with Neural-Network Quantum States
Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.