Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification

TL;DR

This paper introduces an improved parallel quantum annealing method for training Quantum Boltzmann Machines in supervised medical image classification, achieving comparable results to CNNs with faster training times.

Contribution

It presents an enhanced parallel annealing approach for QBMs, enabling supervised learning on medical images with reduced computational costs and training time.

Findings

Achieved comparable accuracy to CNNs on MedMNIST dataset.

Reduced training time by nearly 70% using parallel annealing.

Demonstrated feasibility of QBMs in real-world medical image tasks.

Abstract

Exploiting the fact that samples drawn from a quantum annealer inherently follow a Boltzmann-like distribution, annealing-based Quantum Boltzmann Machines (QBMs) have gained increasing popularity in the quantum research community. While they harbor great promises for quantum speed-up, their usage currently stays a costly endeavor, as large amounts of QPU time are required to train them. This limits their applicability in the NISQ era. Following the idea of No\`e et al. (2024), who tried to alleviate this cost by incorporating parallel quantum annealing into their unsupervised training of QBMs, this paper presents an improved version of parallel quantum annealing that we employ to train QBMs in a supervised setting. Saving qubits to encode the inputs, the latter setting allows us to test our approach on medical images from the MedMNIST data set (Yang et al., 2023), thereby moving closer to real-world applicability of the technology. Our experiments show that QBMs using our approach already achieve reasonable results, comparable to those of similarly-sized Convolutional Neural Networks (CNNs), with markedly smaller numbers of epochs than these classical models. Our parallel annealing technique leads to a speed-up of almost 70 % compared to regular annealing-based BM executions.