Quantum computing has emerged as a potential catalyst for addressing computational bottlenecks that increasingly constrain biotechnologies, including drug discovery, structural biology, and precision medicine. As biological datasets grow in scale and complexity, classical high-performance computing struggles to efficiently model molecular interactions and high-dimensional biological systems. Recent advances in hybrid quantum–classical algorithms and quantum-enhanced machine learning have enabled early proof-of-concept applications on noisy intermediate-scale quantum (NISQ) devices, offering new strategies for molecular simulation, pattern recognition, and optimization. In this opinion article, we critically assess where quantum computing may deliver realistic near-term value for biomedicine, emphasize hybrid workflows as the most viable path forward, and outline how quantum technologies could complement, not replace, existing computational paradigms as the field progresses toward fault-tolerant systems.