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arXiv Machine Learning By Christopher Rector, Jiangwen Sun

A quantum k-nearest neighbors implementation using magnitude-aware comparisons

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The paper introduces a practical quantum encoding technique that allows Euclidean distance calculation between vectors with different L2 norms by appending a single component to each vector. This method is applied to the quantum k‑nearest neighbors (QKNN) problem, offering a simpler and more qubit‑efficient implementation than existing approaches. The authors provide an implementation and benchmark it against a recent Euclidean‑distance QKNN method, showing improved performance in simulations with realistic noise due to the reduced qubit count and more coherent circuits.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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