# H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/h-qnn-a-hybrid-quantum-classical-neural-network-for-improved-binary-image/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Muhammad Asfand Hafeez,Arslan Munir,Hayat Ullah |
| Citations | 32 |
| DOI | 10.3390/ai5030070 |
| Fields | Biochemistry, Genetics and Molecular Biology,Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/2673-2688/5/3/70/pdf?version=1724070940 |
| OpenAlex ID | https://openalex.org/W4401698596 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Hayat Ullah](https://scholariq.org/researchers/hayat-ullah/)

## Paper primary topic

- [Machine Learning and ELM](https://scholariq.org/topics/machine-learning-and-elm/)

## Paper topics

- [Machine Learning and ELM](https://scholariq.org/topics/machine-learning-and-elm/)
- [Quantum Computing Algorithms and Architecture](https://scholariq.org/topics/quantum-computing-algorithms-and-architecture/)
- [Spectroscopy Techniques in Biomedical and Chemical Research](https://scholariq.org/topics/spectroscopy-techniques-in-biomedical-and-chemical-research/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.