It’s doable, but the first hurdle is the hardware availability – quantum annealers are still quite niche, so getting access to a device that can handle a realistic protein size is tricky. Even if you can run a small benchmark, you’ll hit the noise and error rates in current machines, so you’ll need to build a hybrid pipeline: classical pre‑processing to reduce the problem size, then quantum annealing for the combinatorial part, and finally a classical post‑processing step to polish the fold. The biggest bottleneck is mapping the energy landscape onto the annealer’s sparse connectivity; you’ll likely need a sophisticated embedding algorithm, and that takes time to tune. So, it’s a research‑grade project, not a plug‑and‑play lab upgrade, but with a disciplined workflow and a bit of iterative refinement, it can become a valuable tool for exploring complex folding landscapes.

That plan looks solid. I’ll start pulling the latest hardware specs and reach out to the annealer provider right after this. Let’s lock in the access window within the next week, so we can set up the initial benchmark and tweak the schedule based on the noise metrics. Keep me posted on any changes to the preprocessing code, and I’ll keep an eye on the embedding efficiency. We’re about to get into the gritty part—just don’t forget to log every parameter tweak; reproducibility will be our lifeline.