Published May 5, 2025 | Version v1
Dataset Open

Dataset and Code for Quantum-AI Enhanced Biosensor for Environmental Toxin Detection: A Synthetic Data Approach

Authors/Creators

  • 1. Karunya Institute of Science and Technology

Contributors

  • 1. ROR icon Karunya University

Description

This repository contains the datasets, code, and supplementary figures for the study "Quantum-AI Enhanced Biosensor for Environmental Toxin Detection: A Synthetic Data Approach". The study presents a quantum-AI enhanced biosensor that leverages synthetic data generated via Qiskit’s Aer simulator to classify five environmental toxin classes (control, lead, mercury, cadmium, arsenic) with high accuracy (up to 96.04%).

For more details, see the README.md file in the repository.

Files

Quantum_Journal.zip

Files (400.4 MB)

Name Size
md5:2a0bbd26a5bf923d622a715d5a10dee9
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md5:8e9806a7e1e5f6f223e6dd59979eff17
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Additional details

Software

Programming language
Python , Jupyter Notebook
Development Status
Active

References

  • World Health Organization. (2023). Global Health Estimates: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021. Geneva: WHO
  • Smith, J., & Doe, A. (2022). Quantum Sensing for Environmental Monitoring: Advances and Challenges. Journal of Quantum Technology, 15(3), 123-135