How Quantum Computing Could Change The Landscape Of Material Science And Innovation

Abstract

Quantum computing has become a revolution in computer science since it has managed to solve problems that cannot be solved computationally in classical systems. The simulation of complex quantum systems in the field of material science has been an essential task since the 1980s, as computer resources that are needed to represent those systems generate exponentially as a system expands (Feynman, 1982; Georgescu et al., 2014). In the following study, the impact of advancing quantum algorithms and hybrid quantum-classical modeling on discovering materials, designing drugs, and producing sustainable technologies will be studied (Aspuru-Guzik et al., 2005). We consider both important quantum algorithms, such as Variational Quantum Eigensolver (Peruzzo et al., 2014) or Quantum Phase Estimation (Kitaev, 1995), that have already demonstrated their potential in modeling of molecular structures. By application of simulated datasets and benchmarking methods conforming to new frameworks (McArdle et al., 2020), we see that we would improve the computational costs drastically, and at the same time be more accurate when dealing with specific classes of materials with our quantum-enhanced methods. The potential outcomes of the findings are associated with a paradigm shift on an acceleration pace of innovation in the sphere of material design (Bauer et al., 2020). The paper ends with a conclusion that points out the necessity of additional studies in several areas that need to be solved in error correction, noise reduction, and scalable quantum architectures so that all these advantages can be made a reality. 

Key words: Quantum computing, Material science, quantum algorithm, hybrid model, materials

discovery, simulation, innovation.