An Empirical Evaluation of Quantum Annealing-Based Image Classification Using Discriminative Quantum Boltzmann Machines

An Empirical Evaluation of Quantum Annealing-Based Image Classification Using Discriminative Quantum Boltzmann Machines

Abstract:

The Boltzmann machine has been highly influential in the development of artificial intelligence, serving as a foundational framework for energy-based models and neural network research. However, its direct applications in modern deep learning have been severely limited due to computational constraints. Classical sampling methods have consistently proven to be inefficient, rendering the processing of high-dimensional inputs practically infeasible. Therefore, alternatives like the Restricted Boltzmann Machine (RBM) have been introduced, sacrificing expressiveness for faster computations. In contrast, Quantum Boltzmann Machines can efficiently sample from approximate Boltzmann distributions when implemented using quantum algorithms such as quantum annealing. Empirical results suggest that this approach can yield a more efficient sampling process than classical methods, enabling more effective exploration of energy landscapes while reducing computational overhead. Additionally, this also makes full connectivity possible, preserving the expressiveness of the original BM. Nonetheless, to the best knowledge of the author, only a sparse amount of other studies have explored the capability of QBMs for supervised learning. This is particularly true for an application-driven context using real quantum hardware. Thus, the primary goal of this work is the evaluation of the practical effectiveness of QBMs utilizing discriminative learning for the classification of real-world image data using a novel embedding approach to save expensive Quantum Processing Unit time. This is done by employing discriminative QBMs, which always clamp the input units to a data point regardless of the current phase. The model can therefore learn the conditional distribution of a label given a data point. The results demonstrate competitive performance compared to discriminative BMs trained with simulated annealing and discriminative RBMs, while also indicating a slight reduction in the number of training epochs required. Additionally, the embedding approach proposed in this work significantly accelerated sampling, with an average speedup of 69.65% over the conventional embedding.

Author:

Mark Vorapong Seebode

Advisors:

Jonas Stein, Daniëlle Schuman, Claudia Linnhoff-Popien

Student Thesis | Published June 2025 | Copyright © QAR-Lab
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