Quantum Spectral Clustering Framework via Compact Circuit Structures
This work proposes Variational Quantum Approximated Spectral Clustering (VQASC), an unsupervised learning method that uses quantum circuits to perform spectral clustering efficiently. It extends classical spectral clustering by using a quantum circuit structure with linear depth and a training strategy employing fewer parameters than the data size, leading to better accuracy on datasets like Iris and MNIST.