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Springer series in materials science

Quantum-Based Molecular Dynamics Simulations with Applications to Industrial Problems

Christian F. A. Negre, Anders M. N. Niklasson, Antonio Redondo

Published Jan 1, 2021• 2 citations
Publisher
DOI
10.1007/978-3-030-18778-1_15

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In the past decade, many hybrid quantum-classical algorithms have been applied to various industrial fields, reflecting the growing interest in NISQ devices. For example, in the field of chemistry, many studies were conducted to calculate the energies of eigenstates with high precision. However, there is still little research on molecular dynamics using quantum computers, because traditional approaches based on variational quantum algorithms are not suitable for the long-time steps of molecular dynamics. In this study, we propose a quantum circuit learning model that estimates the eigenvalues of a given molecular Hamiltonian for an arbitrary molecular configuration. Our model must first be costly to train; however, once trained, the resulting trained model can eliminate procedures that required variational optimization each time the molecular coordinates are updated in the previous variational approach. By using the proposed model, we demonstrate that the proposed model can be used to perform simple molecular dynamics simulations, such as Langevin dynamics and NVE simulation. We also show several applications of our model inspired by classical machine learning.
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Quantum-computing application studies often stop at the implementation of an isolated circuit or the solution of a small demonstration problem. Industrial adoption requires a more complete engineering process involving problem formulation, quantum encoding, algorithm selection, execution, verification, resource estimation, comparison with strong classical baselines, and assessment of operational relevance. This paper presents an application-oriented framework for QForge, extending its role as a quantum-computing design, emulation, verification, and algorithmdevelopment platform. The framework maps quantum and hybrid methods—including the Quantum Approximate Optimization Algorithm, Variational Quantum Eigensolver, Grover search, Shor period finding, quantum kernels, and amplitude-estimation extensions—to six application classes: industrial optimization, logistics and energy systems, molecular simulation, financial modelling, cybersecurity, quantum machine learning, and education. To reduce premature or misleading application claims, the paper introduces a six-stage evidence model called the QForge Application Readiness Levels (QARL), progressing from problem identification to operational validation. It also defines a reproducible application gallery, classical-baseline discipline, end-to-end time accounting, and structured evidence reports. The paper deliberately distinguishes classical emulation from physical quantum execution and does not claim quantum advantage. Instead, QForge is positioned as an engineering environment in which application hypotheses can be formulated, verified, benchmarked, and prepared for future hybrid high-performance and quantum-computing infrastructures.