Talk · International Conference on Computational Science and Computational Intelligence · December 1, 2021

Quantum Machine Learning: A Case Study of Grover’s Algorithm

Bikram Khanal, Pablo Rivas, Javier Orduz, Alibek Zhakubayev

Abstract

The complexity of searching algorithms in classical computing is a perpetual researched field. Quantum computers and quantum algorithms can compute these problems faster, and, in addition, machine learning implementation could provide a prominent way to boost quantum technology. We call quantum machine learning to this novel set of tools coming from artificial intelligence and quantum mechanics. To achieve our purpose, we focus on applications on quantum machine learning; in particular, we propose a review and exploration of topics such as variational quantum algorithms, kernel methods, and a review of Grover’s algorithm (GA) as a quantum classifier. We start with the GA exploration to achieve this goal, which is a quantum search algorithm that achieves quadratic speedup over optimal classical search implementation. This paper implements a GA exploration that includes a concept summary and implementation, considering only AND, XOR, and OR gates. We also discuss potential in quantum machine learning.

Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum Mechanics and Applicationsquantum machine learningGrover's algorithmquantum search

Quantum Machine Learning: A Case Study of Grover’s Algorithm

Abstract

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