Measurement-Based Quantum Clustering Algorithms
- URL: http://arxiv.org/abs/2302.00566v1
- Date: Wed, 1 Feb 2023 16:38:27 GMT
- Title: Measurement-Based Quantum Clustering Algorithms
- Authors: Srushti Patil, Shreya Banerjee, Prasanta K. Panigrahi
- Abstract summary: Two measurement-based clustering algorithms are proposed in this paper.
The Euclidean distance metric is used as a measure of similarity between the data points.
The bound for each cluster is determined based on the number of ancillae used.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In machine learning, the key approach to finding clusters out of unlabelled
datasets is unsupervised learning algorithms. In this paper, two novel
measurement-based clustering algorithms are proposed. The Euclidean distance
metric is used as a measure of similarity between the data points. The key idea
of quantum parallelism and quantum entanglement is used for clustering. The
bound for each cluster is determined based on the number of ancillae used.
Another quantum-inspired algorithm is proposed based on unsharp measurements
where we construct a set of effect operators with a gaussian probability
amplitude for clustering. We implemented algorithms on a concentric circle data
set, the Churrtiz data set of cities, and the Wisconsin breast cancer dataset.
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