Quantum machine learning

“Detecting Quantum and Classical Phase Transitions via Unsupervised Machine Learning of the Fisher Information Metric”, [2408.03418] by V. Kasatkin, E. Mozgunov, N. Ezzell, D. A. Lidar.

“Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and fixed points”, [2402.08721] by P. Singkanipa, D.A. Lidar.

“Charged particle tracking with quantum annealing-inspired optimization”, Quantum Machine Intelligence 3, 27 (2021), by A. Zlokapa, A. Anand, J-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu [link]

“Identification of driver genes for severe forms of COVID-19 in a deeply phenotyped young patient cohort”, Science Translational Medicine (2021), by R. Carapito, R. Li, J. Helms, C. Carapito, S. Gujja, V. Rolli, R. Guimaraes, J. Malagon-Lopez, P. Spinnhirny, R. Mohseninia, A. Hirschler, L. Muller, P. Bastard, A. Gervais, Q. Zhang, F.s Danion, Y. Ruch, M. Schenck-Dhif, O. Collange, T.-N. Chamaraux-Tran, A. Molitor, A. Pichot, A. Bernard, O. Tahar, S. Bibi-Triki, H. Wu, N. Paul, S. Mayeur, A. Larnicol, G. Laumond, J. Frappier, S. Schmidt, A. Hanauer, C. Macquin, T. Stemmelen, M. Simons, X. Mariette, O. Hermine, S. Fafi-Kremer, B. Goichot, B. Drenou, K. Kuteifan, J. Pottecher, P.-M. Mertes, S. Kailasan, J. Aman, E. Pin, P. Nilsson, A. Thomas, A. Viari, D. Sanlaville, F. Schneider, J. Sibilia, P.-L. Tharaux, J.-L. Casanova, Y. Hansmann, D. Lidar, M. Radosavljevic, J.R. Gulcher, F. Meziani, C. Moog, T.W. Chittenden, S. Bahram [link]

“Quantum processor-inspired machine learning in the biomedical sciences”, Patterns 2, 100246 (2021) by R. Li, S. Gujja, S. Bajaj, O. Gamel, N. Cilfone, J. Gulcher, D. A. Lidar and T. Chittenden [link]

“Quantum adiabatic machine learning with zooming”, Phys. Rev. A 102, 062405, by A. Zlokapa, A. Mott, J-R. Vlimant, J. Job, D. A. Lidar and M. Spiropulu [link]

“Limitations of error corrected quantum annealing in improving the performance of Boltzmann machines”, Quantum Science and Technology 5, 045010 (2020), by R. Li, T. Albash and D. A. Lidar [link]

“Quantum annealing versus classical machine learning applied to a simplified computational biology problem”, npj Quant. Info. 4, 14 (2018), by R. Y. Li, R. Di Felice, R. Rohs and D. A. Lidar [link]

“Solving a Higgs optimization problem with quantum annealing for machine learning”, Nature 550, 375 (2017), A. Mott, J. Job, J. R. Vlimant, D. A. Lidar, and M. Spiropulu

“Quantum Adiabatic Machine Learning”, Quantum Info. Process. 12, 2027  (2013), by K. Pudenz and D.A. Lidar. [link]