Research

The research activities on Quantum Computing at Politecnico di Milano cover several areas and topics:

Applied Quantum Computing

  • Pellini, R., Ferrari Dacrema, M.
    Analyzing the effectiveness of quantum annealing with meta-learning.
    Quantum Machine Intelligence 6, 48 (2024).
    https://doi.org/10.1007/s42484-024-00179-8
  • Carugno, C., Ferrari Dacrema, M., Cremonesi, P.
    Adaptive Learning for Quantum Linear Regression.
    International Conference on Quantum Computing and Engineering  (2024).
  • Turati, G., Ferrari Dacrema, M., Cremonesi, P.
    Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances.
    International Conference on Quantum Computing and Engineering  (2023).
    https://doi.org/10.1109/QCE57702.2023.00053
  • Mato, K.; Mengoni, R.; Ottaviani, D.; Palermo, G.;
    Quantum molecular unfolding.
    Quantum Science and Technology (2022), 7, 3.
    https://doi.org/10.1088/2058-9565/ac73af
  • Carugno, C., Ferrari Dacrema, M., Cremonesi, P.
    Evaluating the job shop scheduling problem on a D-wave quantum annealer.
    Nature Scientific Reports 12, 6539 (2022).
    https://doi.org/10.1038/s41598-022-10169-0
  • Ferrari Dacrema, M., Moroni F., Nembrini R., Ferro N., Faggioli G., Cremonesi, P.
    Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers,
    SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
    https://doi.org/10.1145/3477495.3531755
  • Nembrini, R.; Ferrari Dacrema, M.; Cremonesi, P.
    Feature Selection for Recommender Systems with Quantum Computing.
    Entropy (2021), 23, 970.
    https://doi.org/10.3390/e23080970
  • Ferrari Dacrema, M.; Nembrini, R.; Zhou, T.; Cremonesi, P.
    Quantum Annealing Linear Regression For Collaborative Filtering Recommendations.
    European Quantum Technologies Conference, 2021.
  • Ferrari Dacrema, M.; Felicioni, N.; Cremonesi, P.
    Personalizing Video Recommendation Layout with Quantum Annealing.
    European Quantum Technologies Conference, 2021.
  • Ferrari Dacrema, M.; Felicioni, N.; Cremonesi, P.
    Optimizing the Selection of Recommendation Carousels with Quantum Computing.
    Fifteenth ACM Conference on Recommender Systems, 2021, 691–696.
    https://dl.acm.org/doi/abs/10.1145/3460231.3478853

Quantum Computing Algorithms

  •  Simone Perriello, Alessandro Barenghi, Gerardo Pelosi:
    A Complete Quantum Circuit to Solve the Information Set Decoding Problem.
    IEEE International Conference on Quantum Computing and Engineering, QCE 2021
    https://doi.org/10.1109/QCE52317.2021.00056
  • Simone Perriello, Alessandro Barenghi, Gerardo Pelosi:
    A Quantum Circuit to Speed-Up the Cryptanalysis of Code-Based Cryptosystems.
    Security and Privacy in Communication Networks – 17th EAI International Conference, SecureComm 2021 (ed. Springer)
    https://doi.org/10.1007/978-3-030-90022-9_25

Quantum Machine Learning

  • Armando Bellante, Alessandro Luongo, Stefano Zanero:
    Quantum algorithms for SVD-based data representation and analysis.
    Quantum Machine Intelligence 4.2 (2022) (ed. Springer)
    https://doi.org/10.1007/s42484-022-00076-y
  • Armando Bellante, Stefano Zanero:
    Quantum matching pursuit: A quantum algorithm for sparse representations.
    Poster session at QIP 2022.
    Physical Review A, 2022, 105.2: 022414
    https://doi.org/10.1103/PhysRevA.105.022414
  • Armando Bellante:
    Quantum data representations for audio and natural language processing.
    Quantum natural language processing 2022 (org. by Cambridge Quantum Computing)
    Link to the talk
  • Armando Bellante, Gopikrishnan Muraleedharan, Rolando Somma:
    Solving the quantum simulation problem via signal analysis.
    2021 Virtual Theoretical Division Lightning Talk Series (Los Alamos National Laboratory)
    https://doi.org/10.2172/1836977
  • Alessandro Luongo, Armando Bellante:
    The quantumalgorithms.org project.
    IEEE, International Conference on Quantum Computing and Engineering, QCE 2021, Workshop:
    Developing Effective Methodologies to Teach Quantum Information Science to Early-Stage Learners
  • Armando Bellante, Alessandro Luongo:
    Quantum algorithms for NLP: LSA, QSFA and CA.
    Quantum week of fun, Quantum natural language processing 2020 (org. by Cambridge Quantum Computing)
    Link to the talk