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Quantum machine learning: A comprehensive review of integrating AI with quantum computing for computational
Raghavendra M Devadas1, Sowmya T2
1Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
Methodsx
|May 7, 2025
Summary
Quantum Machine Learning (QML) merges quantum computing and AI for powerful problem-solving. This review explores QML
Area of Science:
- Quantum Computing
- Artificial Intelligence
- Machine Learning
Background:
- Quantum Machine Learning (QML) integrates quantum computation with artificial intelligence.
- It leverages quantum phenomena like superposition and entanglement for enhanced data processing.
- QML offers potential solutions for complex computational problems beyond classical capabilities.
Purpose of the Study:
- To provide a comprehensive overview of the current state of Quantum Machine Learning.
- To explore advancements from quantum-enhanced classical ML to native quantum algorithms and hybrid frameworks.
- To highlight QML applications and future research directions.
Main Methods:
- Review of theoretical underpinnings of QML.
- Analysis of practical applications across various industries.
- Identification of current challenges and future research avenues.
Main Results:
- QML demonstrates potential for exponential speed-ups in machine learning tasks.
- Applications span optimization, drug discovery, and secure communications.
- Significant challenges include noisy qubits, error correction, and data encoding limitations.
Conclusions:
- QML presents a transformative potential for industries like healthcare, finance, and logistics.
- Interdisciplinary research is crucial to overcome existing technical hurdles.
- Continued development is essential for realizing the full promise of QML.
Keywords:
Artificial intelligenceQuantum Machine LearningQuantum computingQuantum machine learningQuantum neural networksMore Related Videos
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