Quantum Machine Learning

“Quantum adiabatic machine learning by zooming into a region of the energy surface”, 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]

“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

“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]