That’s an intriguing idea, but I’d caution that a quantum annealer is good at solving optimization problems, not necessarily decoding an unknown script. The Voynich manuscript, for example, has a structure that might benefit from pattern‑matching algorithms, but you’d still need a massive, accurately labeled dataset to train any model. As for ancient libraries, the layout would depend on surviving architectural plans and inscriptions—quantum annealing could help us test different arrangements, but it’s more of a tool for hypothesis testing than for discovery itself. Still, the intersection of advanced computation and historical research is a fertile ground for new stories.

Sure thing. First, you need clean, structured data—things like floor plans, floor‑plan metadata, maps, architectural drawings, even archaeological site reports. Most of that is in PDFs, CAD files, or scanned images, so you’d have to digitize and standardise it, maybe using OCR for labels and GIS for spatial coordinates. The volume isn’t huge compared to genomic datasets, but the variety is, so you’ll spend a good chunk of time cleaning and normalising. Once you’ve got a set of constraints—room sizes, corridor lengths, relative positions—you can feed those into a cost function for the annealer. The trick is to make sure the constraints actually capture the historical reality; otherwise the “best fit” will just be mathematically neat, not historically useful. So, it’s doable, but the data pipeline is going to be the hardest part.