<p>Traditional drug development poses significant financial and temporal costs, whereas drug repurposing emerges as a cost-effective and efficient alternative. As large-scale biological networks proliferate, computational drug repurposing has become feasible, yet accurately capturing intricate heterogeneous network structures remains a persistent challenge. To address this challenge, we introduced a novel approach, called DRQuantum: Drug Repurposing via Quantum walks. Unlike random walks, quantum walks dispense with independence and harness quantum entanglement to simultaneously explore multiple paths, enabling faster traversal of networks. Moreover, DRQuantum accounts for both the local and global network structures. In this study, we constructed a heterogeneous multi-layer network by integrating drug-drug, disease-disease and protein-protein interaction networks. We then employed quantum walks to learn low-dimensional feature representations of nodes in these heterogeneous networks, ultimately inferring candidate drugs for repurposing beyond their original indications. Consequently, we observed that DRQuantum outperforms traditional drug repurposing methods in terms of AUROC, AUPRC and accuracy. Additionally, case studies for several specific diseases further validate the practical utility of our proposed method.</p>