Education.
2014. PhD in Mathematics at University of Trento (Evaluation: excellent).
2011. Master Degree in Physics at University of Trento (Evaluation: 110/110 cum laude).
Habilitations.
National habilitation (ASN) for Associate Professor in Mathematical Physics.
Academic career.
Current position (2023-present): Assistant Professor (rtda). Department of Mathematics, Alma Mater Studiorum - Università di Bologna.
Past positions:
2020-2023: Assistant Professor (rtda). Department of Information Engineering and Computer Science, Università di Trento.
2015-2019: Postdoc at Department of Mathematics, Università di Trento. P.I. of the project "Research and development of quantum algorithms and quantum cryptographic protocols" funded by Fondazione Caritro (2017-2019).
Teaching.
2020-2022. Master course "Quantum Machine Learning", University of Trento.
2018. Phd course "Introduction to Quantum Information" at Information and Communication Technology School, University of Trento.
2016. PhD course "Foundations of Quantum Information and Quantum Cryptography" for the doctoral school in Mathematics, University of Trento.
2012-2019. Teaching assistance for courses in: geometry, mathematical analysis, discrete mathematics.
Research interests.
-) Mathematical foundations of quantum theories;
-) Quantum Information and computation;
-) Hybrid quantum-classical algorithms;
-) Optimization;
-) Quantum machine learning;
-) Quantum communication and cryptography;
-) Quantum logic;
Research activity.
Main results:
-) Characterization of states and observables within geometric Hamiltonian formulation of quantum mechanics with applications to quantum information;
-) Geometric description of quantum mutual information;
-) Novel quantum cryptographic protocols based on open-loop control schemes and two-way transmission of entangled states;
-) Quantization prescription of a fuzzy logic deforming the product t-norm;
-) Definition and characterization of a hybrid quantum-classical algorithm for quantum learning. In particular, I focused on learning of "problem Hamiltonians" in the context of adiabatic quantum computing.
-) Definition and characterization of quantum-inspired machine learning algorithms for classification problems.
Patents.
-) Incoherent source for intraparticle entanglement (Inventors: S. Mazzucchi, V. Moretti, M. Pasini, D. Pastorello, L. Pavesi. Number: 102020000005521). Filed: 2020. Granted: 2022.
