Quantum Techniques in Machine Learning Dipartimento Di
Quantum Techniques in Machine Learning Dipartimento di Informatica Universit`adi Verona, Italy November 6{8, 2017 Contents Abstracts 9 Marco Loog | Surrogate Losses in Classical Machine Learning (Invited Talk) . 11 Minh Ha Quang | Covariance matrices and covariance operators in machine learning and pattern recognition: A geometrical framework (Invited Talk) . 12 Jonathan Romero, Jonathan Olson, Alan Aspuru-Guzik | Quantum au- toencoders for efficient compression of quantum data . 13 Iris Agresti, Niko Viggianiello, Fulvio Flamini, Nicol`oSpagnolo, Andrea Crespi, Roberto Osellame, Nathan Wiebe, and Fabio Sciarrino | Pattern recognition techniques for Boson Sampling Validation . 15 J. Wang, S. Paesani, R. Santagati, S. Knauer, A. A. Gentile, N. Wiebe, M. Petruzzella, A. Laing, J. G. Rarity, J. L. O'Brien, and M. G. Thompson | Quantum Hamiltonian learning using Bayesian inference on a quantum photonic simulator . 18 Luca Innocenti, Leonardo Banchi, Alessandro Ferraro, Sougato Bose and Mauro Paternostro | Supervised learning of time independent Hamiltonians for gate design . 20 Davide Venturelli | Challenges to Practical End-to-end Implementation of Quan- tum Optimization Approaches for Combinatorial problems (Invited Talk) . 22 K. Imafuku, M. Hioki, T. Katashita, S. Kawabata, H. Koike, M. Maezawa, T. Nakagawa, Y. Oiwa, and T. Sekigawa | Annealing Computation with Adaptor Mechanism and its Application to Property-Verification of Neural Network Systems . 23 Simon E. Nigg, Niels Niels L¨orch, Rakesh P. Tiwari | Robust quantum optimizer with full connectivity . 25 William Cruz-Santos, Salvador E. Venegas-Andraca and Marco Lanzagorta | Adiabatic quantum optimization applied to the stereo matching problem . 26 Alejandro Perdomo Ortiz | Opportunities and Challenges for Quantum-Assisted Machine Learning in Near-Term Quantum Computers .
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