Quantum Synthetic Molecular Dynamics: Advanced Medical Innovations Through Entangled Phenomena of Nucleic Acids

EasyChair Preprint no. 13837, version 2

Abstract

The frontier of Quantum Synthetic Molecular Dynamics Simulation (QSDS) is advancing medical research by exploring complex molecular dynamics in drug interactions and enzyme activities. Our approach uses quantum mechanics to solve challenging optimization problems such as the Selective Traveling Salesman Problem (sTSP) and the Traveling Salesman Problem (TSP), reflecting complex bio-molecular interactions. Employing sophisticated quantum algorithms based on Quadratic Unconstrained Binary Optimization (QUBO) models, we aim to accurately predict molecular behavior and elucidate nucleic acid functions. We integrate cutting-edge technologies like D-Wave Quantum Annealing with Quantum Support Vector Machines (QSVM), Quantum Recurrent Neural Networks (QRNN), and Variational Quantum Algorithms (VQAs) incorporating Trotter-type formulations, grounded in an in-depth analysis of Potential Energy Surfaces (PESs) and the Born-Oppenheimer approximation. Leveraging Pennylane AI, we bridge quantum and classical hardware to enhance the development and testing of hybrid algorithms, aiming to transform our understanding of molecular dynamics and drive advances in drug discovery and genetic research, showcasing the impactful potential of quantum technologies in healthcare and medicine. Our Quantum Synthetic Molecular Dynamics Simulation (QSDS) research leverages the QM9 dataset, containing nearly 134,000 molecules with detailed properties, to test the effectiveness of our quantum algorithms in predicting molecular stability, reactivity, and interaction potentials. We also explore Quantum Structure Activity Relationship (QSAR) models to investigate entangled eigenvalues and Wannier localization, providing new insights into quantum healthcare applications, especially in enhancing medication efficacy.

Keyphrases: Born-Oppenheimer Approximation, D-Wave Quantum Annealing, drug discovery, Entangled Eigenvalues, Genetic Material Analysis, hybrid quantum-classical algorithms, molecular dynamics, Nucleic Acid Functions, Optimization Problems, Pennylane AI, Potential Energy Surfaces (PESs), QM9 Dataset, Quadratic Unconstrained Binary Optimization (QUBO), quantum algorithms, Quantum Healthcare Applications, quantum mechanics, Quantum Recurrent Neural Networks (QRNN), quantum sensing, Quantum Structure Activity Relationship (QSAR) Models, Quantum Support Vector Machines (QSVM), Quantum Synthetic Molecular Dynamics Simulation (QSDS), Trotter-type Formulations, Variational Quantum Algorithms (VQAs), Wannier Localization

@Booklet{EasyChair:13837,
  author = {Kiran Mai Narnavaram and Dan Chia-Tien Lo and Xinyue Zhang and Bobin Deng},
  title = {Quantum Synthetic Molecular Dynamics: Advanced Medical Innovations Through Entangled Phenomena of Nucleic Acids},
  howpublished = {EasyChair Preprint no. 13837},

  year = {EasyChair, 2024}}