Quantum Clustering Algorithm using the Wheel of Tomography

Quantum Clustering Algorithm using the Wheel of Tomography

© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Shradha Deshmukh, Preeti Mulay
DOI :  10.14445/22315381/IJETT-V70I5P214

How to Cite?

Shradha Deshmukh, Preeti Mulay, "Quantum Clustering Algorithm using the Wheel of Tomography," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 111-119, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P214

A quantum k-means clustering algorithm is introduced by integrating the quantum paradigm to enhance the efficiency of the classical k-means algorithm. Firstly, each vector and k cluster centers are prepared to be in a quantum superposition, then utilized to compute the similarities in parallel. Secondly, the quantum amplitude estimation is applied to convert the similarities into the quantum bit. Finally, the most similar center of the vector is obtained from the qubits by using the quantum algorithm with the help of tomography to determine the minimum distances. Using the IBMQ simulator, completed the performance analysis for air pollution, which involved a two-dimensional dataset. The paper discussed a qk-means quantum clustering algorithm, which first maps the classical data into quantum states and performs distance calculation and updation using the quantum circuits. The paper proposed a general, parallelized, and competitive version of qk-means clustering, observing the outcomes of this performance analysis for multiple combinations of quantitative data series. Results show that the IBMQ simulator can overcome the classical k-means clustering problem of completion time and accuracy.

Quantum Clustering, Quantum Machine Learning, Incremental Learning, Quantum Incremental learning, qk-means Algorithm.

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