Machine Learning

“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]

“Reverse quantum annealing of the p-spin model with relaxation”, Phys. Rev. A. 101, 022331 (2020), by G. Passarelli, K. Yip, D. A. Lidar, H. Nishimori and P. Lucignano [link]

“Unconventional machine learning of genome-wide human cancer data”, [1909.06206],  by R. Li, S. Gujja, S. Bajaj, O. Gamel, N. Cilfone, J. Gulcher, D. A. Lidar and T. Chittenden

“Quantum adiabatic machine learning with zooming”, [1908.04480], by A. Zlokapa, A. Mott, J-R. Vlimant, J. Job, D. Lidar and M. Spiropulu

“Charged particle tracking with quantum annealing-inspired optimization”, [1908.04475], by A. Zlokapa, A. Anand, J-R. Vlimant, J. Duarte, J. Job, D. Lidar and M. Spiropulu

“Quantum annealing versus classical machine learning applied to a simplified computational biology problem”, npj Quant. Info. 414 (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]