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arXiv · 2307.14459

Training Quantum Boltzmann Machines with Coresets

Joshua Viszlai·Teague Tomesh·Pranav Gokhale·Eric Anschuetz·Frederic T. Chong
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Abstract

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.

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Joshua Viszlai, Teague Tomesh, Pranav Gokhale, Eric Anschuetz, Frederic T. Chong. 2023-07-26. Training Quantum Boltzmann Machines with Coresets. https://arxiv.org/abs/2307.14459

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