Quantum computer models a chemical reaction

T. M. Rocha Filho, Joaquim José Soares Neto

A. Smirnov, Sergey E. Savel'ev, Lev G. Mourokh +1 more

Amandeep Singh Bhatia, Shenggen Zheng

In recent years, the modeling interest has increased significantly from the molecular level to the atomic and quantum scale. The field of computational chemistry plays a significant role in designing computational models for the operation and simulation of systems ranging from atoms and molecules to industrial-scale processes. It is influenced by a tremendous increase in computing power and the efficiency of algorithms. The representation of chemical reactions using classical automata theory in thermodynamic terms had a great influence on computer science. The study of chemical information processing with quantum computational models is a natural goal. In this paper, we have modeled chemical reactions using two-way quantum finite automata, which are halted in linear time. Additionally, classical pushdown automata can be designed for such chemical reactions with multiple stacks. It has been proven that computational versatility can be increased by combining chemical accept/reject signatures and quantum automata models.

Nathan Wiebe, Markus Reiher, Krysta M. Svore +2 more

Jincheng Zhang

This paper explores the potential of topological quantum computation (TQC) to revolutionize the simulation of complex chemical reactions. Traditional computational methods often struggle to accurately model the intricate interactions between atoms within these reactions. We propose a novel framework utilizing topological phases to represent these interactions, aiming for a more compact and efficient simulation model than existing approaches. The core claim is that TQC offers a pathway to significantly improve the fidelity and speed of simulating complex chemical processes, potentially enabling breakthroughs in drug discovery, materials science, and fundamental chemical understanding. The paper details the design and implementation of a TQC simulation platform, highlighting key challenges and preliminary results demonstrating the framework's potential for accurate modeling.

Takumi Kato, Koji Okuwaki, Yuji Mochizuki +2 more