Training Quantum Boltzmann Machines with Coresets
Joshua Viszlai Teague Tomesh Pranav Gokhale Eric Anschuetz Frederic T. Chong
Recent work has proposed and explored using coreset techniques for quantum
algorithms that operate on classical data sets to accelerate the applicability
of these algorithms on near-term quantum devices. We apply these ideas to
Quantum Boltzmann Machines (QBM) where gradient-based steps which require Gibbs
state sampling are the main computational bottleneck during training. By using
a coreset in place of the full data set, we try to minimize the number of steps
needed and accelerate the overall training time. In a regime where
computational time on quantum computers is a precious resource, we propose this
might lead to substantial practical savings. We evaluate this approach on 6x6
binary images from an augmented bars and stripes data set using a QBM with 36
visible units and 8 hidden units. Using an Inception score inspired metric, we
compare QBM training times with and without using coresets.