# Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines

arXiv: https://arxiv.org/abs/2311.15966 · HTML: https://mlsanity.com/paper/2311.15966/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [A scoping review of transfer learning research on medical image analysis using ImageNet](https://mlsanity.com/paper/2004.13175/) (2020)
- [Application of Quantum Annealing to Training of Deep Neural Networks](https://mlsanity.com/paper/1510.06356/) (2015)
- [Restricted Boltzmann Machines: Introduction and Review](https://mlsanity.com/paper/1806.07066/) (2018)
- [Generative training of quantum Boltzmann machines with hidden units](https://mlsanity.com/paper/1905.09902/) (2019)
- [Simulated Quantum Annealing Can Be Exponentially Faster than Classical Simulated Annealing](https://mlsanity.com/paper/1601.03030/) (2016)
- [SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training](https://mlsanity.com/paper/2301.02601/) (2023)
- [Histopathological Cancer Detection Using Hybrid Quantum Computing](https://mlsanity.com/paper/2302.04633/) (2023)
- [Hybrid Quantum Machine Learning Assisted Classification of COVID-19 from Computed Tomography Scans](https://mlsanity.com/paper/2310.02748/) (2023)

## Built on by (most cited first, 3 of 3)

- [On the Transfer of Knowledge in Quantum Algorithms](https://mlsanity.com/paper/2501.14120/) (2025)
- [Quantum Boltzmann Machines using Parallel Annealing for Medical Image Classification](https://mlsanity.com/paper/2507.14116/) (2025)
- [Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing](https://mlsanity.com/paper/2603.02958/) (2026)

## Abstract (arXiv)

Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML). Existing approaches, however, only utilize gate-based Variational Quantum Circuits for the quantum part of these procedures. In this work we present an approach to employ Quantum Annealing (QA) in QTL-based image classification. Specifically, we propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline to learn the classification of real-world, large-scale data such as medical images through supervised training. We demonstrate our approach by applying it to the three-class COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices. Using Simulated Annealing as a stand-in for actual QA, we compare our method to classical transfer learning, using a neural network of the same order of magnitude, to display its improved classification performance. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score and needs less training epochs to do this.