<ul data-eligibleForWebStory="false"><li>Quantum Machine Learning (QML) is a field combining quantum computing and artificial intelligence to enhance data-driven tasks by leveraging quantum computational advantages.</li><li>The review explores how QML can address computational bottlenecks in classical machine learning, particularly for complex datasets.</li><li>Key areas of focus include quantum data encoding, learning theory, optimization techniques, and applications in quantum chemistry and sensing.</li><li>Challenges like Noisy Intermediate-Scale Quantum (NISQ) devices are discussed, highlighting the need for quantum-native algorithms and improved error correction for practical deployment.</li></ul>

Advances in Machine Learning: Where Can Quantum Techniques Help?

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