Home / Papers / Project Report-Quantum Machine Learning

Project Report-Quantum Machine Learning

88 Citations2017
Amrit Singhal, amrits
journal unavailable

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.

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.