Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing [article]

Ryan Babbush, Vasil Denchev, Nan Ding, Sergei Isakov, Hartmut Neven
2014 arXiv   pre-print
Quantum annealing is a heuristic quantum algorithm which exploits quantum resources to minimize an objective function embedded as the energy levels of a programmable physical system. To take advantage of a potential quantum advantage, one needs to be able to map the problem of interest to the native hardware with reasonably low overhead. Because experimental considerations constrain our objective function to take the form of a low degree PUBO (polynomial unconstrained binary optimization), we
more » ... ploy non-convex loss functions which are polynomial functions of the margin. We show that these loss functions are robust to label noise and provide a clear advantage over convex methods. These loss functions may also be useful for classical approaches as they compile to regularized risk expressions which can be evaluated in constant time with respect to the number of training examples.
arXiv:1406.4203v1 fatcat:nmri7mwwp5asrnymdmdpurdd24