IEEE International Conference for Convergence in Technology (I2CT)

Quantum Supervised Clustering Algorithm for Big Data

Arit Kumar Bishwas, Ashish Mani, Vasile Palade

Published Apr 1, 2018• 3 citations

Abstract

Supervised clustering is the process of grouping unlabeled objects by using some specific objectives and any supervised learning technique. In traditional clustering algorithms, the similarity measure is based on some distance finding formulations. In supervised clustering, this similarity measure is trained with the help of asupervised learning method. In this paper, we have designed a quantum supervised clustering algorithm and analyzed the overall runtime complexity of the algorithm. In our approach, we have trained the similarity measure model with quantum support vector machine. This quantum mechanically trained similarity measure model is used in the quantum K-Means algorithm, instead of the traditional distance-based similarity measure functions, for clustering unlabeled objects. Our analysis demonstrates that the proposed approach exhibits exponential speed up when compared to the classical implementation.

DOI: 10.1109/i2ct.2018.8529799

Related papers

Big Data Quantum Support Vector Clustering.

Arit Kumar Bishwas, Ashish Mani, Vasile Palade

Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, broadly known as unsupervised learning. Support vector clustering algorithm is a well-known clustering algorithm based on support vector machines and Gaussian kernels. In this paper, we have investigated the support vector clustering algorithm in quantum paradigm. We have developed a quantum algorithm which is based on quantum support vector machine and the quantum kernel (Gaussian kernel and polynomial kernel) formulation. The investigation exhibits approximately exponential speed up in the quantum version with respect to the classical counterpart.

Apr 29, 20184 citations

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

Arit Kumar Bishwas, Ashish Mani, Vasile Palade

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.

International Conference for Emerging Technology (INCET)Jun 1, 20200 citations

Quantum unsupervised and supervised learning on superconducting processors

Abhijat Sarma, Rupak Chatterjee, Kaitlin Gili +1 more

Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the data, the computation time for training and using these statistical models grows quickly. Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it becomes less computationally expensive to implement for large datasets as compared to its classical counterpart.

arXiv:1909.04226Quantum Information and ComputationJun 1, 202017 citations

Quantum Support Vector Machine for Big Data Classification

Patrick Rebentrost, Masoud Mohseni, Seth Lloyd

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

arXiv:1307.0471Physical Review LettersSep 25, 20141,951 citations

Quantum support vector machine for big feature and big data classification.

Patrick Rebentrost, Masoud Mohseni, Seth Lloyd

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized linear and non-linear binary classifier, can be implemented on a quantum computer, with exponential speedups in the size of the vectors and the number of training data examples. At the core of the algorithm is a non-sparse matrix simulation technique to efficiently perform a principal component analysis and matrix inversion of the training data kernel matrix. We thus provide an example of a quantum big feature and big data algorithm and pave the way for future developments at the intersection of quantum computing and machine learning.

Jul 1, 201338 citations

Comparative Analysis of Classical and Quantum Approach for Unsupervised K-Means Algorithm

G. Murali, Paruchuri Pravallika, Narreddy Aravind Reddy +2 more

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.

Jul 2, 20210 citations