Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease
Summary: arXiv:2604.13608v1 Announce Type: cross
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 various design choices. These choices include classical-to-quantum data encoding, quantum circuit architecture, measurement strategy, and the number of shots used in the experiments. This article presents a comprehensive exploration of these design spaces specifically aimed at diagnosing Chronic Kidney Disease (CKD).
In our study, we utilized a carefully curated and preprocessed clinical dataset to benchmark 625 different HQNN models. These models were developed by combining:
- Five encoding schemes
- Five entanglement architectures
- Five measurement strategies
- Five different shot settings
To ensure a fair and robust evaluation of these models, we employed 10-fold stratified cross-validation. Each model was assessed on a separate test set using a comprehensive array of performance metrics. These metrics included:
- Accuracy
- Area Under the Curve (AUC)
- F1-score
- A composite performance score
Our findings revealed strong and non-trivial interactions between the choices of encoding and circuit architecture. Notably, we observed that high performance does not necessarily require models with large parameter counts or complex circuit designs. In particular, we discovered that compact architectures, when combined with appropriate encodings—such as the IQP (Instantaneous Quantum Polynomial time) model with Ring entanglement—can achieve a superior balance between accuracy, robustness, and efficiency.
Furthermore, beyond simply analyzing absolute performance, our study provides actionable insights into how different design dimensions influence the learning behavior of HQNNs. These insights could pave the way for optimizing quantum neural network architectures for various applications in medical diagnostics and beyond.
The exploration of HQNNs for CKD diagnosis represents a significant step forward in the field of quantum machine learning. As the technology matures, the implications of our findings could extend to various other complex diseases, highlighting the potential of quantum computing to revolutionize healthcare diagnostics.
In conclusion, our research underscores the importance of thoughtful design in the development of hybrid quantum neural networks. By systematically exploring the design space, we can enhance the performance of these innovative models and contribute to the growing body of knowledge in quantum machine learning applications.