Here, we leverage quantum computing for novel anti-malarial drug discovery, specifically addressing artemisinin resistance in Plasmodium falciparum. Classical molecular dynamics reveals PfK13 mutations (e.g., Y493H, C580Y) significantly alter protein dynamics, particularly in the BTB-domain, impacting PfK13-Cullin binding and ubiquitination. Quantum algorithms, such as quantum simulation of molecular interactions and quantum machine learning for binding affinity prediction, offer unprecedented accuracy and speed. We propose to simulate the quantum mechanical behavior of mutated PfK13 and its interactions with potential drug candidates. This approach will elucidate the intricate electronic and vibrational changes underlying resistance, enabling the design of highly effective, resistance-proof anti-malarial compounds by precisely targeting the altered protein dynamics.