# QuantumBoostNet: Hybrid Classical-Quantum Cardiac View Identification

arXiv: https://arxiv.org/abs/2608.27302 · HTML: https://mlsanity.com/paper/2608.27302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Classical-to-quantum convolutional neural network transfer learning](https://mlsanity.com/paper/2208.14708/) (2022)
- [EchoFM: Foundation Model for Generalizable Echocardiogram Analysis](https://mlsanity.com/paper/2410.23413/) (2024)
- [Parallel Hybrid Networks: an interplay between quantum and classical neural networks](https://mlsanity.com/paper/2303.03227/) (2023)
- [Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms](https://mlsanity.com/paper/2510.07317/) (2025)
- [Lean classical-quantum hybrid neural network model for image classification](https://mlsanity.com/paper/2412.02059/) (2024)
- [Readout-Side Bypass for Residual Hybrid Quantum-Classical Models](https://mlsanity.com/paper/2511.20922/) (2025)
- [Hybrid Quantum-Classical Learning for Multiclass Image Classification](https://mlsanity.com/paper/2508.18161/) (2025)
- [Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits](https://mlsanity.com/paper/2603.16973/) (2026)
- [Practical Quantum-Classical Feature Fusion for complex data Classification](https://mlsanity.com/paper/2512.19180/) (2025)

## Abstract (arXiv)

Accurate identification of the correct view or angle in cardiac ultrasound (echocardiogram) is critical for cardiologic imaging, precise anatomical interpretation, and reducing clinical errors. Most state-of-the-art classical models perform well on standard benchmarks but give suboptimal results in specialized medical imaging due to high noise levels. To address these challenges, this work proposes the hybrid classical-quantum architecture QuantumBoostNet, which combines a classical backbone with two heads: one classical and one quantum, a parametrized 10-qubit quantum circuit. The main contribution of this work is training in two stages, with an adaptive transition between heads controlled by a mixing parameter that monitors loss dynamics. Extensive experiments show that QuantumBoostNet outperforms the implemented baselines under matched training conditions. Statistically significant gains appear on FashionMNIST (pt=9.91!×!10-6) and MNIST (pt=1.29!×!10-5), with a less significant improvement on the echocardiography task (pt=0.0711). The model also shows robustness to noise. These findings support continued development of hybrid classical-quantum models for specialized medical imaging applications.