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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Coalition of explainable artificial intelligence and quantum computing in precision medicine.

Soumyadeep Ray¹, Pronaya Bhattacharya², Ebrahim A Mattar³

¹Department of Biotechnology, Amity University, Newtown, Kolkata, 700135, West Bengal, India.

Computational and Structural Biotechnology Journal

|December 4, 2025

Summary

This survey explores how Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) are converging to advance precision medicine. It highlights hybrid quantum-explainable AI frameworks for drug discovery and diagnostics, aiming for trustworthy healthcare.

Keywords:

Explainable artificial intelligence Precision medicine Quantum computing Quantum machine learning

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Published on: June 13, 2025

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Last Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Quantum Computing
Explainable Artificial Intelligence
Precision Medicine

Background:

The integration of advanced AI and quantum computing presents new opportunities for personalized healthcare.
Explainable Artificial Intelligence (XAI) is crucial for understanding complex models in medicine.

Purpose of the Study:

To survey the convergence of XAI and QC in precision medicine from 2018-2025.
To introduce a taxonomy of hybrid and quantum-explainable approaches.
To identify barriers and future research directions.

Main Methods:

Review of quantum algorithms, quantum-machine-learning models, and XAI techniques.
Evaluation of NISQ hardware and encoding constraints.
Comparison of interpretability methods (SHAP, LIME, QSHAP, QLRP, TSBA).
Demonstration via case studies in drug toxicity prediction and disease flare forecasting.

Main Results:

Development of hybrid variational-quantum pipelines with XAI explanations.
Identification of practical barriers including noise, data encoding, regulation, and privacy.
Benchmarking of clinical quantum advantage and scalable QXAI frameworks.

Conclusions:

The convergence of XAI and QC offers a path toward trustworthy, scalable quantum-enabled precision healthcare.
Interdisciplinary efforts are needed to overcome current limitations and realize clinical quantum advantage.