# Hybrid quantum transfer learning for crack image classification on NISQ hardware

arXiv: https://arxiv.org/abs/2307.16723 · HTML: https://mlsanity.com/paper/2307.16723/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [A hybrid quantum image edge detector for the NISQ era](https://mlsanity.com/paper/2203.12072/) (2022)
- [Improved FRQI on superconducting processors and its restrictions in the NISQ era](https://mlsanity.com/paper/2110.15672/) (2021)
- [Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images](https://mlsanity.com/paper/2112.09493/) (2021)

## Built on by (most cited first, 4 of 4)

- [Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits](https://mlsanity.com/paper/2603.16973/) (2026)
- [Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation](https://mlsanity.com/paper/2410.10713/) (2024)
- [Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning](https://mlsanity.com/paper/2602.00048/) (2026)
- [Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images](https://mlsanity.com/paper/2603.28995/) (2026)

## Abstract (arXiv)

Quantum computers possess the potential to process data using a remarkably reduced number of qubits compared to conventional bits, as per theoretical foundations. However, recent experiments have indicated that the practical feasibility of retrieving an image from its quantum encoded version is currently limited to very small image sizes. Despite this constraint, variational quantum machine learning algorithms can still be employed in the current noisy intermediate scale quantum (NISQ) era. An example is a hybrid quantum machine learning approach for edge detection. In our study, we present an application of quantum transfer learning for detecting cracks in gray value images. We compare the performance and training time of PennyLane's standard qubits with IBM's qasmsimulator and real backends, offering insights into their execution efficiency.