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International Conference for Emerging Technology (INCET)

A Leap from Randomized to Quantum Clustering with Support Vector Machine - A Computation Complexity Analysis

Arit Kumar Bishwas, Ashish Mani, Vasile Palade

Published Jun 1, 2020• 0 citations
Publisher

Abstract

Supervised machine learning deals with developing complex non-linear models, which can be used later to predict the output for a known input. Clustering is usually treated as an unsupervised machine learning task, but we can formulate a solution to a clustering problem by using a supervised classification algorithm [1]. However, these classification algorithms are highly computationally intensive in nature, so the overall complexity in designing a clustering solution is often very costly from an implementation point of view. The more data we use, the more computational power is required too. Recent advancements in quantum computing show promising advantages in dealing with this kind of computational issues we face while training a complex machine-learning algorithm. In this paper, we do a theoretical investigation on the runtime complexity of algorithms, from classical to randomized, and then to quantum frameworks, when designing a clustering algorithm. The analysis shows significant computational advantages with a quantum framework as compared to the classical and randomized versions of the implementation.

DOI
10.1109/incet49848.2020.9154060

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Quantum algorithms are being extensively researched nowadays, seeing the potential to provide exponential speedup compared to classical algorithm execution. This speedup can play a significant role in machine learning, where training a model is usually prolonged. Training a machine learning model requires compressing large vectors into small computational units, and quantum computers inherently are fast at compressing the large vectors into small computations and tensor products. The present work aims to compare the K-means, a classical machine learning algorithm, and Q-means, a Quantum algorithm. Here in this work, we use q-means, a quantum algorithm for clustering, a canonical problem in unsupervised machine learning. The q-means algorithm has convergence and precision guarantees similar to k-means. [14]It outputs with high probability a good approximation of the k cluster centroids like the classical algorithm. The experimental results are demonstrated, which show a significant difference between k-means and q-means algorithms.
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