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Research Topic Ideas in Quantum Machine Learning

Research Topic Ideas in Quantum Machine Learning

   Quantum Machine Learning (QML) is an innovative type of machine learning which incorporates quantum information computing with classical machine learning. Quantum machine learning uses bits and quantum operations to improve computational speed and data storage. The significant benefits of quantum machine learning are improved run time, learning capacity, and learning efficiency. Quantum Machine Learning algorithms are categorized as QML algorithm, quantum-inspired ML, and Hybrid quantum-classical ML.
    Quantum Machine Learning (QML) techniques are more successful in many real-world applications compared to traditional machine learning such as big data classification, forecasting series, spam detection, image compression, medical domain including cervical cancer detection, electronic calculations, decision games, natural language processing (NLP), recommendation systems, speech recognition, image classification, and electrocardiogram signals classification. Future directions in QML are small-scale quantum computers, Limited quantum bits, encoding methods, and developing new QML techniques by using quantum neural networks (QNN), quantum deep Learning, quantum-enhanced ML in several fields.