# Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease

arXiv: https://arxiv.org/abs/2604.13608 · HTML: https://mlsanity.com/paper/2604.13608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 15 Apr 2026.

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

- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Connecting ansatz expressibility to gradient magnitudes and barren plateaus](https://mlsanity.com/paper/2101.02138/) (2021)
- [Financial Fraud Detection using Quantum Graph Neural Networks](https://mlsanity.com/paper/2309.01127/) (2023)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models](https://mlsanity.com/paper/2308.05237/) (2023)
- [Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation](https://mlsanity.com/paper/2103.11357/) (2021)
- [Comparative Performance Analysis of Quantum Machine Learning Architectures for Credit Card Fraud Detection](https://mlsanity.com/paper/2412.19441/) (2024)
- [The MCC-F1 curve: a performance evaluation technique for binary classification](https://mlsanity.com/paper/2006.11278/) (2020)
- [Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training](https://mlsanity.com/paper/2304.12950/) (2023)
- [FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm](https://mlsanity.com/paper/2511.10062/) (2025)
- [ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks](https://mlsanity.com/paper/2402.09146/) (2024)
- [QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2503.08759/) (2025)

## Built on by (most cited first, 2 of 2)

- [Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices](https://mlsanity.com/paper/2605.22097/) (2026)
- [QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation](https://mlsanity.com/paper/2606.04689/) (2026)

## Abstract (arXiv)

Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends on design choices such as classical-to-quantum data encoding, quantum circuit architecture, measurement strategy and shots. In this paper, we present a comprehensive design space exploration of HQNNs for Chronic Kidney Disease (CKD) diagnosis. Using a carefully curated and preprocessed clinical dataset, we benchmark 625 different HQNN models obtained by combining five encoding schemes, five entanglement architectures, five measurement strategies, and five different shot settings. To ensure fair and robust evaluation, all models are trained using 10-fold stratified cross-validation and assessed on a test set using a comprehensive set of metrics, including accuracy, area under the curve (AUC), F1-score, and a composite performance score. Our results reveal strong and non-trivial interactions between encoding choices and circuit architectures, showing that high performance does not necessarily require large parameter counts or complex circuits. In particular, we find that compact architectures combined with appropriate encodings (e.g., IQP with Ring entanglement) can achieve the best trade-off between accuracy, robustness, and efficiency. Beyond absolute performance analysis, we also provide actionable insights into how different design dimensions influence learning behavior in HQNNs.