Title: HARLI CQUINN: Higher Adjusted Randomness with Linear In Complexity QUantum INspired Networks for K-Means Show Chinese title

Title: HARLI CQUINN：用于K均值的线性复杂度量子启发网络的更高调整随机性

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. Show Chinese abstract

Abstract: 我们对比了量子k均值算法的最小实现与经典k均值算法。 通过经典模拟结果，我们展示了量子性能，其表现与经典k均值算法相当甚至更优。 我们提供了对已知和实验数据集测试用例的准确性基准。 尽管对量子k均值算法进行了大量研究，但我们的方法揭示了之前未探索的方法改进。 编码步骤可以是最小化的，与现有方法相比，经典数据可以更直接地导入量子状态。 提出的量子启发算法在准确性和调整后的兰德指数（ARI）方面优于原始的经典k均值算法。 通过研究多种编码策略，我们提供了对量子计算聚类技术的细致见解。