Research
Condensed matter
Research interests include theory of superconductivity and superfluidity: BCS-BEC crossover, pseudogap and fluctuating phenomena in ultracold fermions and multiband superconductors and superfluids. Shape resonances, Lifshitz transitions, and dimensional crossover in nanostructured superconductors and superfluids. Diagrammatic and numerical methods for strongly interacting fermions and low dimensional and multicomponent systems are adopted. Large international collaborations focus on multiband superconductivity and superfluidity on superfluid graphene-based devices to observe and exploit high-temperature electron-hole superfluidity.
High-Temperature Superfluidity in Double-Bilayer Graphene,
A. Perali, D. Neilson, and A. R. Hamilton,
Phys. Rev. Lett. 110, 146803 (2013).
Screening of pair fluctuations in superconductors with coupled shallow and deep bands: A route to higher-temperature superconductivity,
L. Salasnich, A.A. Shanenko, A. Vagov, J.A. Aguiar, A. Perali,
Physical Review B 100, 064510 (2019).
Chester supersolid of spatially indirect excitons in double-layer semiconductor heterostructures,
Sara Conti, Andrea Perali, Alexander R Hamilton, Milorad V Milošević, François M Peeters, David Neilson,
Physical Review Letters 130, 057001 (2023).
Quantum Many-Body Physics
Ultracold matter refers to a state of matter in which particles have been cooled to extremely low temperatures, often near absolute zero. This cooling creates unique quantum effects and allows for the precise control and manipulation of atoms and molecules. Researchers in ultracold matter physics investigate phenomena like Bose-Einstein condensation and degenerate Fermi gases, enabling the study of fundamental quantum behaviors and the development of applications in fields such as quantum computing and precision measurement. Ultracold matter offers insights into the quantum world at its most extreme, with temperatures approaching the lowest possible energy state.
Attractive Solution of Binary Bose Mixtures: Liquid-Vapor Coexistence and Critical Point
G. Spada, S. Pilati, S. Giorgini
Phys. Rev. Lett. 131, 173404 (2023)
Berezinskii-Kosterlitz-Thouless phase transition with Rabi-coupled bosons,
Koichiro Furutani, Andrea Perali, and Luca Salasnich,
Phys. Rev. A 107, L041302 (2023).
Numerical methods for Quantum matter and Complex systems
Solving quantum many-body problems requires advanced computational algorithms and high-performance computers. Our group develops stochastic methods based on quantum Monte Carlo algorithms, as well as novel approaches based on deep learning methods. These techniques, also used in combination, allow performing accurate simulations of complex many-body systems, shedding light on the role of interactions, disorder, and quantum fluctuations of the macroscopic behaviour.
Deep learning nonlocal and scalable energy functionals for quantum Ising models
E. Costa, R. Fazio, S. Pilati
Phys. Rev. B 108, 125113 (2023)
Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks,
B. McNaughton, M. V. Milošević, A. Perali, and S. Pilati
Phys. Rev. E 101, 053312 (2020).
Laboratory of superconductivity
The SuperNano laboratory focuses on the experimental characterization of transport and structural properties of superconducting systems, including nanostructured films, organic materials, and multicomponent superconductors. A He closed-cycle Gifford McMohan based cryostat is available in the laboratory. A new He closed-cycle cryostat, equipped with a superconducting magnet up to 12 T, will be availble next year. Structural properties are studied by SEM and micro-Raman.
Dimensional crossover and incipient quantum size effects in superconducting niobium nanofilms,
Nicola Pinto, S. Javad Rezvani, Andrea Perali, Luca Flammia, Milorad V. Milošević, Matteo Fretto, Cristina Cassiago & Natascia De Leo,
Scientific Reports 8, 4710 (2018).
Complex Phase-Fluctuation Effects Correlated with Granularity in Superconducting NbN Nanofilms,
Meenakshi Sharma, Manju Singh, Rajib K. Rakshit, Surinder P. Singh, Matteo Fretto, Natascia De Leo, Andrea Perali, and Nicola Pinto
Nanomaterials 12, 4109 (2022).
Adiabatic and gate-based quantum computing
In the long-term, quantum computers promise to solve relevant computational tasks that are intractable for classical computers. However, the true potential of near-term devices in realistic conditions is still unclear. Our group develops stochastic and deep-learning algorithms to predict the behavior of quantum computers, focusing both on universal devices based on the gate model and on quantum-enhanced optimization in the adiabatic model of quantum computing. We also investigate hybrid approaches, e.g., combining classical deep learning with quantum devices, to identify early application of quantum computers.
Challenges of variational quantum optimization with measurement shot noise
G. Scriva, N. Astrakhantsev, S. Pilati, G. Mazzola
Physical Review A 109, 032408 (2024)
Supervised learning of random quantum circuits via scalable neural networks
S. Cantori, D. Vitali, S. Pilati
Quantum Science and Technology 8, 025022 (2023)
Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
G. Scriva, E. Costa, B. McNaughton, S. Pilati