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PhD in Physics, Sapienza University of Rome, Italy
Topics: physics-inspired algorithms, quantum computing, machine learning and quantum machine learning
Group: Fisica AI&QC group
Supervisors: Stefano Giagu, Fabio Sciarrino
Andrea Cacioppo
Curriculum vitae - March 2026
Download PDFI am a physicist with 6+ years of experience in machine learning research and development. Currently finishing my PhD in physics. Since February 2026, I am a Researcher at Italy's Istituto Superiore di Sanità, in the Department of Food Safety, Nutrition and Veterinary Public Health.
Education
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M.Sc. in Physics, Sapienza University of Rome, Italy
Thesis: “Deep learning for the parameter estimation of tight-binding Hamiltonians”
Supervisors: Stefano Giagu, Stefan Bauer
Grade: 109/110
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B.Sc. in Physics, Sapienza University of Rome, Italy
Thesis: “Hidden Markov model”
Supervisor: Luciano Pietronero
Grade: 110/110 with honors
Work experience
- present
Researcher, Istituto Superiore di Sanità, Italy
Dept. of Food Safety, Nutrition and Veterinary Public Health
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Machine Learning Consulting, Italy & Switzerland
ML consulting applied to diverse industry problems.
- Primis Group SRL, Milan, Italy: determine best Machine Learning solutions tailored to LiDAR and satellite data, design of an anomaly detection algorithm for LiDAR data (contract of Rete Ferroviaria Italiana SPA)
- Hypercube SA, Lugano, Switzerland: application of Machine Learning techniques to the detection of time series anomalies
- Grid +, Rome, Italy: Automatic analysis of legal documents and anomaly detection
- Individual clients, Italy: training NNs to solve PDEs in finance - implementation of diffusion models - training NNs on incomplete datasets - invoice reconciliation using an online LLM
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Research Associate, Technical University of Munich, Germany
Topics: classical-quantum compound channels and algorithms for the automatic generation of quantum graph states
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Research Internship, Max Planck Institute for Intelligent Systems, Tübingen, Germany
Topics: Deep learning for estimating tight-binding Hamiltonians, quantum machine learning models and their connection with kernel methods
Teaching
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"Python for scientific programming", Istituto Superiore di Sanità, Italy
Task: lectures and lab tutoring
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"Foundations of Programming with Laboratory", Sapienza University of Rome, Italy
Task: lectures and lab tutoring
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Mathematics, physics and computer science, Individual clients, Italy
Task: individual tutoring for university students
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"Quantum networking", Technical University of Munich, Germany
Task: tutoring
Students supervised
- Lorenzo Colantonio — "Quantum Diffusion generative models on variational quantum circuits" (Roma, 2023).
- Federico Scarpati — "Physics informed Graph Neural Networks for Graph Coloring Problems" (Roma, 2024).
Awards and grants
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Research grant, Sapienza University of Rome, Italy
"Development of quantum machine learning algorithms" - 1000 €
Excellence program for honor students, Sapienza University of Rome, Italy
Conference talks
Quantum Computing @ INFN, Milan, Italy, Talk
"Quantum enhanced fraud detection: a comparative study on real-world banking data"
Quantum Computing @ INFN, Padova, Italy, Talk
"Quantum diffusion models for quantum data learning"
QAIXIAQ2023 Workshop, Rome, Italy, Talk
"Quantum diffusion models using parameterized quantum circuits for data denoising"
ISIT, 2021 IEEE International Symposium on Information Theory, Talk
"Compound channel capacities under energy constraints and application"
Languages
- Italian - native
- English - fluent
- German - beginner
Skills
- Python, PyTorch, Linux, LaTeX, Git
Publications
- Andrea Cacioppo, Lorenzo Colantonio, Simone Bordoni, and Stefano Giagu. Hybrid and hardware-oriented approaches for quantum diffusion models. In 2025 International Joint Conference on Neural Networks (IJCNN), 2025.
- Lorenzo Colantonio, Andrea Cacioppo, Federico Scarpati, and Stefano Giagu. Efficient graph coloring with neural networks: A physics-inspired approach for large graphs. arXiv preprint arXiv:2408.01503, 2024.
- Andrea Cacioppo, Lorenzo Colantonio, Simone Bordoni, and Stefano Giagu. Quantum Diffusion Models. arXiv preprint arXiv:2311.15444, 2023.
- Andrea Cacioppo, Janis Nötzel, and Matteo Rosati. Compound Channel Capacities under Energy Constraints and Application. In 2021 IEEE International Symposium on Information Theory (ISIT), pages 640–645. IEEE, 2021.
- Andrea Cacioppo. Deep learning for the parameter estimation of tight-binding Hamiltonians. Master’s thesis, Sapienza Università di Roma, Italy, 2020.