HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means (2509.19395v1)

Abstract: We contrast a minimalistic implementation of quantum k-means algorithm to classical k-means algorithm. With classical simulation results, we demonstrate a quantum performance, on and above par, with the classical k-means algorithm. We present benchmarks of its accuracy for test cases of both well-known and experimental datasets. Despite extensive research into quantum k-means algorithms, our approach reveals previously unexplored methodological improvements. The encoding step can be minimalistic with classical data imported into quantum states more directly than existing approaches. The proposed quantum-inspired algorithm performs better in terms of accuracy and Adjusted Rand Index (ARI) with respect to the bare classical k-means algorithm. By investigating multiple encoding strategies, we provide nuanced insights into quantum computational clustering techniques.