# QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics

arXiv: https://arxiv.org/abs/2010.08031 · HTML: https://mlsanity.com/paper/2010.08031/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Benchmarking MedMNIST dataset on real quantum hardware](https://mlsanity.com/paper/2502.13056/) (2025)
- [Knowledge Graph Embedding Methods for Entity Alignment: An Experimental Review](https://mlsanity.com/paper/2203.09280/) (2022)
- [A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems](https://mlsanity.com/paper/2310.15044/) (2023)
- [Parametric Leaky Tanh: A New Hybrid Activation Function for Deep Learning](https://mlsanity.com/paper/2310.07720/) (2023)
- [M-ar-K-Fast Independent Component Analysis](https://mlsanity.com/paper/2108.07908/) (2021)
- [A Quantum-Powered Photorealistic Rendering](https://mlsanity.com/paper/2211.03418/) (2022)
- [Vectorized Attention with Learnable Encoding for Quantum Transformer](https://mlsanity.com/paper/2508.18464/) (2025)

## Abstract (arXiv)

The ReLU activation function (AF) has been extensively applied in deep neural networks, in particular Convolutional Neural Networks (CNN), for image classification despite its unresolved dying ReLU problem, which poses challenges to reliable applications. This issue has obvious important implications for critical applications, such as those in healthcare. Recent approaches are just proposing variations of the activation function within the same unresolved dying ReLU challenge. This contribution reports a different research direction by investigating the development of an innovative quantum approach to the ReLU AF that avoids the dying ReLU problem by disruptive design. The Leaky ReLU was leveraged as a baseline on which the two quantum principles of entanglement and superposition were applied to derive the proposed Quantum ReLU (QReLU) and the modified-QReLU (m-QReLU) activation functions. Both QReLU and m-QReLU are implemented and made freely available in TensorFlow and Keras. This original approach is effective and validated extensively in case studies that facilitate the detection of COVID-19 and Parkinson Disease (PD) from medical images. The two novel AFs were evaluated in a two-layered CNN against nine ReLU-based AFs on seven benchmark datasets, including images of spiral drawings taken via graphic tablets from patients with Parkinson Disease and healthy subjects, and point-of-care ultrasound images on the lungs of patients with COVID-19, those with pneumonia and healthy controls. Despite a higher computational cost, results indicated an overall higher classification accuracy, precision, recall and F1-score brought about by either quantum AFs on five of the seven bench-mark datasets, thus demonstrating its potential to be the new benchmark or gold standard AF in CNNs and aid image classification tasks involved in critical applications, such as medical diagnoses of COVID-19 and PD.