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  1. Quantum machine learning

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    Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum physics and machine learning. The most common use

  2. Machine learning

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    for machine learning research Predictive analytics Quantum machine learning Machine-learning applications in bioinformatics Seq2seq Fairness (machine learning)

  3. Outline of machine learning

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    Cross-disciplinary fields involving machine learning Adversarial machine learning Predictive analytics Quantum machine learning Robot learning Developmental robotics

  4. Machine learning in physics

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    Applying classical methods of machine learning to the study of quantum systems (sometimes called quantum machine learning) is the focus of an emergent

  5. List of datasets for machine-learning research

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    for machine-learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major

  6. Quantum computing

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    Post-quantum cryptography Quantum annealing Quantum bus Quantum cognition Quantum cryptography Quantum logic gate Quantum machine learning Quantum threshold

  7. Cambridge Quantum Computing

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    has built enterprise applications for quantum chemistry, quantum machine learning, quantum cryptography and quantum finance. CQC was selected as one of

  8. Quantum neural network

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    (which are widely used in machine learning for the important task of pattern classification) with the advantages of quantum information in order to develop

  9. Restricted Boltzmann machine

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    classification,collaborative filtering, feature learning,topic modelling and even many body quantum mechanics. They can be trained in either supervised

  10. Quantum entanglement

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    Quantum entanglement is the physical phenomenon that occurs when a pair or group of particles is generated, interact, or share spatial proximity in a

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