This special session aims to explore the emerging intersection between Quantum Artificial Intelligence (QAI) and Bioinformatics/Biostatistics, bringing together researchers working on quantum algorithms, computational biology, and data-driven life sciences. The rapid growth of high-dimensional and heterogeneous biological data, spanning genomics, transcriptomics, proteomics, single-cell sequencing, and molecular interaction networks, poses significant computational and statistical challenges that often exceed the scalability of classical learning and inference methods. Quantum computing offers alternative computational paradigms for optimization, sampling, and kernel-based models that are central to modern bioinformatics pipelines, opening new opportunities for more efficient analysis of complex biological systems. The session will focus on both methodological advances and application-oriented studies in QAI. Topics of interest include quantum and quantum-inspired learning methods for sequence and phylogenetic analysis, graph-based representations and quantum kernels, biomolecular structure modeling, clustering and classification of omics data, and combinatorial optimization problems arising in molecular design and systems biology. Particular emphasis will be placed on hybrid quantum–classical workflows and implementations compatible with Noisy Intermediate-Scale Quantum (NISQ) devices, addressing realistic constraints such as limited qubits, noise, shallow circuits, and efficient data encoding. Contributions presenting noise-resilient algorithms, resource-aware designs, and systematic benchmarking against state-of-the-art classical approaches are especially encouraged. By fostering dialogue between quantum computing experts and bioinformatics practitioners, this session aims to identify realistic opportunities, clarify current limitations, and define actionable research directions. The expected impact is to strengthen interdisciplinary collaboration and assess the concrete potential of QAI for advancing computational biology.