QM/MM Molecular Dynamics Run within Quantum and Tensor-Based Algorithms towards Topologically Inspired, Accurate Protein Folding

The most accurate approaches to protein folding require molecular dynamics (MD). However, these methods are both slow and extensively thorough, requiring exacting understanding of the solvent environment and its minute details along with a thorough understanding of the MD software itself to add these parts effectively. We aim to improve upon these methods through the investigation of quantum algorithms for MD approaches, as well as topological mathematics on top of these methodologies for further decreases in computational complexity.

Our approach is three-pronged:

1. 1. We evaluate advances in tensor network calculations as a new way to run classical molecular dynamics (MM) simulations, specifically focusing on CHARMM in this work, by resolving RAM bottlenecks by taking the entire MM problem for the whole protein into one, GPU ready algorithm, learning from Tinker-HP [1]
   
   2. We take a quantum approximate method for quantum molecular dynamics models (QM) for approximating symmetry adapted perturbation theory (SAPT) utilizing the basis set given B3LYP classical optimization within VQE [2]
   
   3. We use origami trees [3] to represent proteins in ways which allow a different approach to understanding folded states to replace or augment current QM/MM methods in worst cases.

Our results are forthcoming.