Quantum Machine Learning

Nov 27, 2017
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This project was done as research project in the course CS682: Quantum Computing, in the Fall ‘17 term at IIT Kanpur under Prof. Rajat Mittal, Department of Computer Science and Engineering, IIT Kanpur.

Abstract

The aim of the project is to study two of the most widely used machine learning strategies, namely KNearest Neighbours algorithm and Perceptron Learning algorithm, in a quantum setting, and study the speedups that the quantum modules allow over the classical counterparts.

The study is primarily based on the following 3 papers:

  1. Quantum Perceptron Models, by N. Wiebe, A. Kapoor and K. M. Svore.
  2. Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance, by Y. Ruan, X. Xue, H. Liu, J. Tan, and X. Li.
  3. Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning, by N. Wiebe, A. Kapoor and K. M. Svore.
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Amrit Singhal
MS in Machine Learning

My research interests include machine learning, reinforcement learning and artifical intelligence.

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