Quantum Computing for Electronic Structure and Sensor-Material Discovery

Jincheng Zhang

This paper explores the transformative potential of quantum simulation for accelerating materials discovery. Classical computational methods often struggle with the complexities of simulating quantum mechanical systems, particularly for materials exhibiting strong electron correlations. Quantum simulation offers a fundamentally different approach, leveraging the principles of quantum mechanics to directly model these systems on a quantum computer. This work outlines the key concepts, methodologies, and potential impact of this emerging field. We present a framework for utilizing quantum computers to simulate material properties, including electronic structure, magnetic behavior, and transport characteristics. Specifically, we discuss the use of Variational Quantum Eigensolver (VQE) and Trotterization methods for solving the Schrödinger equation. The core claim is that quantum simulation will revolutionize materials discovery by enabling precise predictions of material properties and accelerating the identification of novel materials with desired characteristics. Furthermore, we address the current limitations and future directions of this technology, emphasizing the need for robust quantum hardware and sophisticated algorithms. ---

P. Sureshkumar, Madhusudan Narayan

Stephanie Law, S. J. J. M. F. Kokkelmans

O.Yaswantha Reddy, G. Jawahar Sagar, Tarkeshwar Mahto +3 more

The urgent need for high-performance, sustainable electric vehicle (EV) batteries has driven the exploration of advanced computational methods to accelerate material discovery. Traditional approaches, such as Density Functional Theory (DFT) and Hartree-Fock, face inherent limitations in simulating the complex quantum behaviors of novel battery materials. This paper introduces a pioneering framework leveraging quantum computing, specifically the Variational Quantum Eigen solver (VQE), to overcome these challenges and optimize solid-state battery materials. We focus on Lithium Thiophosphate ($\mathbf{L i}_{3} \mathbf{P S} \mathbf{4}_{4}$), a promising electrolyte for next-generation batteries, and demonstrate how quantum simulations can provide a deeper understanding of electronic structures and electrochemical reactions at an unprecedented level of precision. By benchmarking quantum results against classical methods, we highlight the transformative potential of quantum algorithms to capture intricate electron correlations and reaction dynamics, offering more accurate predictions for material performance. Our findings suggest that quantum computing not only offers a significant leap in the accuracy of battery material simulations but also paves the way for scalable, data-driven optimization of next-generation energy storage systems.

Rodney S. Ruoff

Ma, Huan, Yi Fan, Jie Liu +3 more

Exploring the potential application of quantum computers in material design and drug discovery has attracted a lot of interest in the age of quantum computing. However, the quantum resource requirement for solving practical electronic structure problems are far beyond the capacity of near-term quantum devices. In this work, we integrate the divide-and-conquer (DC) approaches into the variational quantum eigensolver (VQE) for large-scale quantum computational chemistry simulations. Two popular divide-and-conquer schemes, including many-body expansion~(MBE) fragmentation theory and density matrix embedding theory~(DMET), are employed to divide complicated problems into many small parts that are easy to implement on near-term quantum computers. Pilot applications of these methods to systems consisting of tens of atoms are performed with adaptive VQE algorithms. This work should encourage further studies of using the philosophy of DC to solve electronic structure problems on quantum computers.