Link prediction with swarms of chiral quantum walks

TL;DR

This paper introduces a chiral quantum walk approach with random phases to improve link prediction in protein networks, enhancing robustness and exploration capabilities over non-chiral methods.

Contribution

The study develops a swarm of chiral quantum walks with phase sampling strategies, outperforming non-chiral algorithms in robustness and predictive accuracy.

Findings

Chiral quantum walks are more robust than non-chiral versions.

Phase sampling strategies can balance accuracy and robustness.

Chiral methods outperform classical and non-chiral quantum approaches.

Abstract

Reconstructing protein-protein interaction networks is a central challenge in network medicine, often addressed using link prediction algorithms. Recent studies suggest that quantum walk-based approaches hold promise for this task. In this paper, we build on these algorithms by introducing chirality through the addition of random phases in the Hamiltonian generators. The resulting additional degrees of freedom enable a more diverse exploration of the network, which we exploit by employing a swarm of chiral quantum walks. Thus, we enhance the predictive power of quantum walks on complex networks. Indeed, compared to a non-chiral algorithm, the chiral version exhibits greater robustness, making its performance less dependent on the optimal evolution time--a critical hyperparameter of the non-chiral model. This improvement arises from complementary dynamics introduced by chirality within the swarm. By analyzing multiple phase-sampling strategies, we identify configurations that achieve a practical trade-off: retaining the high predictive accuracy of the non-chiral algorithm at its optimal time while gaining the robustness typical of chirality. Our findings highlight the versatility of chiral quantum walks and their potential to outperform both classical and non-chiral quantum methods in realistic scenarios, including comparisons between successive versions of evolving databases.