36 research outputs found
Quantum-Classical Multiple Kernel Learning
As quantum computers become increasingly practical, so does the prospect of
using quantum computation to improve upon traditional algorithms. Kernel
methods in machine learning is one area where such improvements could be
realized in the near future. Paired with kernel methods like support-vector
machines, small and noisy quantum computers can evaluate classically-hard
quantum kernels that capture unique notions of similarity in data. Taking
inspiration from techniques in classical machine learning, this work
investigates simulated quantum kernels in the context of multiple kernel
learning (MKL). We consider pairwise combinations of several
classical-classical, quantum-quantum, and quantum-classical kernels in an
empirical investigation of their classification performance with support-vector
machines. We also introduce a novel approach, which we call QCC-net
(quantum-classical-convex neural network), for optimizing the weights of base
kernels together with any kernel parameters. We show this approach to be
effective for enhancing various performance metrics in an MKL setting. Looking
at data with an increasing number of features (up to 13 dimensions), we find
parameter training to be important for successfully weighting kernels in some
combinations. Using the optimal kernel weights as indicators of relative
utility, we find growing contributions from trainable quantum kernels in
quantum-classical kernel combinations as the number of features increases. We
observe the opposite trend for combinations containing simpler, non-parametric
quantum kernels.Comment: 15 pages, Supplementary Information on page 15, 6 main figures, 1
supplementary figur