<p>Due to the success of the Standard Model (SM), it is reasonable to anticipate that the signal of new physics (NP) beyond the SM is small. Consequently, future searches for NP and precision tests of the SM will require high luminosity collider experiments. Moreover, as precision tests advance, rare processes with many final-state particles require consideration which demands the analysis of a vast number of observables. The high luminosity produces a large amount of experimental data spanning a large observable space, posing a significant data-processing challenge. In recent years, quantum machine learning has emerged as a promising approach for processing large amounts of complex data on a quantum computer. In this study, we propose quantum searching neighbor (QSN) and variational QSN (VQSN) algorithms to search for NP. The QSN is a classification algorithm. The VQSN introduces variation to the QSN to process classical data. As applications, we apply the (V) QSN in the phenomenological study of the NP at the Large Hadron Collider and muon colliders. Examples are implemented on a real quantum hardware, which confirms reliable performance under noisy conditions. The results indicate that the VQSN demonstrates superior efficiency in the sense of computational complexity to a classical counterpart k-nearest neighbor algorithm, even when dealing with classical data.</p>