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b/R&D/008 QML Algorithms History.md |
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[Quantum ML Algorithms Historical Perspective](https://www.chemicalqdevice.com/quantum-ml-algorithms-historical-perspective) PDF 7/31/23. |
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Synopsis: An early paper to learn Quantum ML basics, as well as some of the common |
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quantum models being researched. <br> |
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1) Emerging Quantum ML Models <br> |
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A) Quantum Support Vector Machine <br> |
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a) Evaluation of an inner product is better on a quantum computer <br> |
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B) Quantum K-Nearest Neighbor Methods <br> |
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a) Efficient calculation of classical distances with quantum computer <br> |
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C) K-Means Quantum Clustering <br> |
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a) Several full quantum routines for clustering are available <br> |
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D) Quantum Decision Trees <br> |
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a) Quantum feature states encode n features into quantum system <br> |
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E) Quantum State Classification with Bayesian Methods <br> |
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a) Reformulation in the language of open quantum systems <br> |
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F) Hidden Quantum Markov Models <br> |
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a) Contain classical models and richer generalization dynamics <br> |
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G) Quantum Neural Networks <br> |
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a) Quantum dots; adiabatic computing; hopfield networks; fuzzy ff <br> |