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Quantum computing in biology is one of the most rapidly evolving fields of technology. Protein folding is one of the key challenges which requires accurate and efficient algorithms with a quick computational time. Structural conformations of proteins with disordered regions need colossal amount of computational resource to map its least energy conformation state. In this regard, quantum algorithms like variational quantum eigensolver (VQE) are applied in the current research work to predict the lowest energy value of 50 peptides of seven amino acids each. VQE is initially used to calculate the energy values over which variational quantum optimization is applied via conditional value at risk (CVaR) over 100 iterations of 500,000 shots each to obtain least ground-state energy value. This is compared to the molecular dynamics-based simulations of 50 ns each to calculate the energy values along with the folding pattern. The results suggest efficient folding outcomes from CVaR-VQE compared to MD-based simulations and HMM-SA. With the ever-expanding quantum hardware and improving algorithms, the problem of protein folding can be resolved to obtain in-depth insights on the biological process and drug design.


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      @akshayuc how would AlphaFold come into play here? Which part of the problem does it solve (or approximately solve) and which part is it not able to address that quantum computers could be good for?