It is very difficult to represent and process 3D structures with Quantum Computing. So the best we can normally do is to do Protein Folding on a 3D Lattice.
But compared to the best the World has w/ Deep Learning in Protein Folding, which works with only 66% accuracy. Quantum Protein Folding is an ab-initio method. Which makes absolutely no assumptions and calculates the Protein Structure starting from a Fasta Sequence only.
The Protein Folds Naturally in ~1ms.
But computationally there are Gazillions of configurations it could fold into. The configuration which has the lowest energy globally is the answer we are seeking.
And the problem of Protein Folding is to find this one Lowest Energy Configuration out of all the Gazillion Possibilities.
Automatski has a Universal Optimization Solver and also a production grade Eigen Solver which can solve Protein Folding with 99%+ accuracy.
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