QUANTUM - QKMEAN
K-mean is the popular unsupervised machine learning algorithm, given M points and k (number of clusters), this algorithm will find k centroids, and then predict the label's new points by comparing the distance between the new point with the centroids. The new points belong to the cluster whose centroid is nearest to the new point.
The Quantum K-mean is an enhanced version of the K-mean by replacing the normal Euclidean distance function (O(N^2)) with the quantum Euclidean distance function (O(log(N)) (N is dimensional of point).
KEY FEATURES
1. High-dimensional Data Handling: Efficiently processes high-dimensional data due to reduced complexity in distance calculations.
2. Scalability: Easily scales to large datasets with logarithmic complexity, suitable for big data applications.
3. Accuracy: Maintains or improves clustering accuracy compared to classical K-means by leveraging precise quantum computations.
4. Parallel Processing Support: Quantum algorithms can perform multiple distance calculations simultaneously, enhancing overall performance.
5. Integration with Quantum Hardware: Designed to work with current quantum hardware and simulators, leveraging advancements in quantum technology.
6. User-friendly Interface: Provides an intuitive interface for setting parameters such as the number of clusters (𝑘) and initializing centroids.
7. Visualization Tools: Includes tools for visualizing clusters, centroids, and the algorithm's iterative process, aiding in understanding and interpretation.