Ancestral sequence reconstruction is one of the most reliable techniques for designing robust biocatalysts. It requires the identification of evolutionarily related sequences, constructing accurate multiple-sequence alignment, and inference of non-existing ancestral sequences. Fully automated workflows for non-experts are available, e.g., FireProt or GRASP . For computational reasons, only up to 150 sequences are often used for the reconstruction, and quantum computing will provide tools for the efficient use of much larger phylogenetic trees . Positioning of insertions and deletions is the most critical step of the entire workflow, determining foldability and expressibility of designs. Quantum parallelism will provide means for more systematic mining of genomic and metagenomic databases , processing much larger sets of sequences, re-running inference and most importantly in silico evaluation of designs with variable locations of insertions and deletions. Solving the Achilles Heel of ancestral reconstruction by more reliable positioning of insertions and deletions will dramatically improve the success of designs.