Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis DOI Creative Commons
Agostino Marengo, Vito Santamato

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 7

Published: May 7, 2025

This paper presents a systematic review of quantum computing approaches to healthcare-related computational problems, with an emphasis on quantum-theoretical foundations and algorithmic complexity. We adopt optimized machine learning methodology—combining Particle Swarm Optimization (PSO) Latent Dirichlet Allocation (LDA)—to analyze the literature identify key research themes at intersection healthcare. A total 63 peer-reviewed studies were analyzed, 41 categorized under first domain 22 second. approach revealed two primary directions: (1) for artificial intelligence in healthcare, (2) healthcare data security. highlight theoretical advances underlying these domains, from novel algorithms biomedical cryptographic protocols securing medical information. gradient boosting classifier further validates our taxonomy by reliably distinguishing between categories research, demonstrating robustness identified themes, accuracy 84.2%, precision 88.9%, recall F1-score 84.5%, area curve 0.875. Interpretability analysis using Local Interpretable Model-Agnostic Explanations (LIME) exposes features each category (e.g., references applications versus blockchain-based security frameworks), offering transparency into literature-driven categorization, latter showing most significant contributions topic assignment (ranging −0.133 +0.128). Our findings underscore that offer potential enhance security, optimize complex diagnostic computations, provide speedups health informatics. also outstanding challenges—such as need scalable error-tolerant hardware integration—that must be addressed translate advancements real-world clinical impact. study emphasizes importance hybrid quantum-classical models cross-disciplinary bridge gap cutting-edge theory its practical

Language: Английский

Advanced TiO2-Based Photocatalytic Systems for Water Splitting: Comprehensive Review from Fundamentals to Manufacturing DOI Creative Commons

Tarek Ahasan,

E.M.N. Thiloka Edirisooriya, Punhasa S. Senanayake

et al.

Molecules, Journal Year: 2025, Volume and Issue: 30(5), P. 1127 - 1127

Published: Feb. 28, 2025

The global imperative for clean energy solutions has positioned photocatalytic water splitting as a promising pathway sustainable hydrogen production. This review comprehensively analyzes recent advances in TiO2-based systems, focusing on materials engineering, source effects, and scale-up strategies. We recognize the advancements nanoscale architectural design, engineered heterojunction of catalysts, cocatalyst integration, which have significantly enhanced efficiency. Particular emphasis is placed crucial role chemistry system performance, analyzing how different sources-from wastewater to seawater-impact evolution rates stability. Additionally, addresses key challenges scaling up these including optimization reactor light distribution, mass transfer. Recent developments artificial intelligence-driven discovery process are discussed, along with emerging opportunities bio-hybrid systems CO2 reduction coupling. Through critical analysis, we identify fundamental propose strategic research directions advancing technology toward practical implementation. work will provide comprehensive framework exploring advanced composite developing efficient scalable multifunctional simultaneous

Language: Английский

Citations

0
Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis DOI Creative Commons
Agostino Marengo, Vito Santamato

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 7

Published: May 7, 2025

This paper presents a systematic review of quantum computing approaches to healthcare-related computational problems, with an emphasis on quantum-theoretical foundations and algorithmic complexity. We adopt optimized machine learning methodology—combining Particle Swarm Optimization (PSO) Latent Dirichlet Allocation (LDA)—to analyze the literature identify key research themes at intersection healthcare. A total 63 peer-reviewed studies were analyzed, 41 categorized under first domain 22 second. approach revealed two primary directions: (1) for artificial intelligence in healthcare, (2) healthcare data security. highlight theoretical advances underlying these domains, from novel algorithms biomedical cryptographic protocols securing medical information. gradient boosting classifier further validates our taxonomy by reliably distinguishing between categories research, demonstrating robustness identified themes, accuracy 84.2%, precision 88.9%, recall F1-score 84.5%, area curve 0.875. Interpretability analysis using Local Interpretable Model-Agnostic Explanations (LIME) exposes features each category (e.g., references applications versus blockchain-based security frameworks), offering transparency into literature-driven categorization, latter showing most significant contributions topic assignment (ranging −0.133 +0.128). Our findings underscore that offer potential enhance security, optimize complex diagnostic computations, provide speedups health informatics. also outstanding challenges—such as need scalable error-tolerant hardware integration—that must be addressed translate advancements real-world clinical impact. study emphasizes importance hybrid quantum-classical models cross-disciplinary bridge gap cutting-edge theory its practical

Language: Английский

Citations

0