Tuesday 26 (Room C3.06) – 16:00 – 18:00
Meeting for the constitution of the AIxIA “AI for Quantum and Quantum for AI” (AIQxQIA) Workgroup – Scope and Objectives
Wednesday 27 (Room C3.06)
10:30 – 10:40 Opening Remarks
10:40 – 11:30 Invited Talk: Tensor Network Machine Learning – Simone Montangero (University of Padua)
SESSION 1 – Quantum for AI (10:30 – 12:30)
11:30 – 11:45 “Quantum Patch-Based Autoencoder for Anomaly Segmentation” – F. Madeira, A. Poggiali and J.M. Lorenz
11:45 – 12:00 “Variational Compression of Circuits for State Preparation” – A. Berti, G. Antonioli, A. Bernasconi, G.M. Del Corso, R. Guidotti and A. Poggiali
12:00 – 12:15 “Qibolab: an open-source hybrid quantum operating system” – S. Bordoni and S. Carrazza
12:15 – 12:30 “Exploring the Role of Hamiltonian Expressibility in Ansatz Selection for Variational Quantum Algorithms” – F. Brozzi, G. Turati, M. Ferrari Dacrema and P. Cremonesi
12:30 – 13:30 LUNCH
SESSION 2 – AI for Quantum (16:00 – 18:00)
16:00 – 16:15 “Conditional Value at Risk enhanced Quantum Local Search” – N.H.H. Phuc, V.H. Nguyen and S.T. Anh
16:15 – 16:25 “Solving quantum circuit compilation problem variants through genetic algorithms” – L. Arufe, R. Rasconi, A. Oddi, R. Varela and M.Á. González
16:25 – 16:40 “An Application of Reinforcement Learning for Minor Embedding in Quantum Annealing” – R. Nembrini, M. Ferrari Dacrema and P. Cremonesi
16:40 – 16:55 “Parameter prediction for Variational quantum algorithms through Sequence modeling” – C. Loglisci, V. Losavio, B. De Carolis, M. Skenduli and D. Malerba
16:55 – 17:10 “Reinforcement Learning for Variational Quantum Circuit Design” – S. Foderà, G. Turati, R. Nembrini, M. Ferrari Dacrema and P. Cremonesi
17:10 – 17:25 “Quantum noise modeling through Reinforcement Learning” – S. Bordoni, S. Carrazza, S. Giagu, A. Papaluca, P. Buttarini and A. Sopena
17:25 – 17:35 “A Two-Phase Quantum Algorithm for the Partial Max-CSP” – M. Baioletti, F. Fagiolo, A. Oddi and R. Rasconi
17:35 – 17:45 “Enumerating Extensions in Abstract Argumentation by Using QUBO” – M. Baioletti, F. Rossi and F. Santini
17:45 – 18:00 Concluding Remarks
Invited Talk
Tensor Network Machine Learning
Simone Montangero – University of Padua
Abstract: We present the concepts of tensor network machine
learning, a quantum-inspired method that naturally bridges classical and
quantum machine learning techniques. We present its application to the
study of event classification at LHCb and review some new elements of
explainability that this approach enables.