Entropy 25, 500 (2023)
Marta Maria Marchese, Alessio Belenchia and Mauro Paternostro
We make use of the powerful formalism of quantum parameter estimation to assess the characteristic rates of a continuous spontaneous localization (CSL) model affecting the motion of a massive mechanical system. We show that a study performed in non-equilibrium conditions unveils the…
Event: School on Quantum Photonics: Principles and Applications Speaker: Angelo Bassi Place: TBAE - Gebze (Turkey) Date: 13 March 2023
Event: QTI Lectures @ CERN Speaker: Giovanni Di Bartolomeo Place: Online Date: 1 March 2023
Event: KOBIT7 conference Speaker: Mauro Paternostro Place: Eskisehir, Turkey Date: 21 Feb 2023
Event: Seminar to high school students Speaker: Angelo Bassi Place: Trieste Date: 18 Feb 2023
Symmetry 15, 480 (2023)
Fabrizio Napolitano et al
Modern physics lays its foundations on the pillars of Quantum Mechanics (QM), which has been proven successful to describe the microscopic world of atoms and particles, leading to the construction of the Standard Model. Despite the big success, the old open questions at its very heart, such…
Entropy 25, 295 (2023)
Kristian Piscicchia et al.
Models of dynamical wave function collapse consistently describe the breakdown of the quantum superposition with the growing mass of the system by introducing non-linear and stochastic modifications to the standard Schrödinger dynamics. Among them, Continuous Spontaneous Localization (CSL) was extensively investigated both theoretically and experimentally. Measurable consequences…
Event: Quantum West Speaker: Angelo Bassi Place: San Francisco Date: 31 Jan 2023
New Journal of Physics 25, 013030 (2023)
Giorgio Zicari, Barış Çakmak, Özgür E Müstecaplıoğlu and Mauro Paternostro
Recent studies have pointed out the intrinsic dependence of figures of merit of thermodynamic relevance—such as work, heat and entropy production—on the amount of quantum coherences that is made available to a system. However, whether coherences hinder or…
Quantum Science and Technology 8, 025004 (2023)
Jonathon Brown, Mauro Paternostro and Alessandro Ferraro
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits—encoded in the states of artificial atoms with no direct coupling—interacting via a common single-mode…