New frontiers of quantum computing in chemical engineering

Stefan Knecht, Erik Donovan Hedegård, Sebastian F. Keller +5 more

Reliable quantum chemical methods for the description of molecules with dense-lying frontier orbitals are needed in the context of many chemical compounds and reactions. Here, we review developments that led to our new computational toolbox which implements the quantum chemical density matrix renormalization group in a second-generation algorithm. We present an overview of the different components of this toolbox.

Ivan Kassal

Arunima K. Singh

Amirhossein Nourbakhshrezaei, Mark Jones, Kaur Kristjuhan +7 more

We use the benefits and components of classical computers every day. However, there are many types of problems which, as they grow in size, their computational complexity grows larger than classical computers will ever be able to solve. Quantum computing (QC) is a computation model that uses quantum physical properties to solve such problems. QC is at the early stage of large-scale adoption in various industry domains to take advantage of the algorithmic speed-ups it has to offer. It can be applied in a variety of areas, such as computer science, mathematics, chemical and biochemical engineering, and the financial industry. The main goal of this paper is to give an overview to chemical and biochemical researchers and engineers who may not be familiar with quantum computation. Thus, the paper begins by explaining the fundamental concepts of QC. The second contribution this publication tries to tackle is the fact that the chemical engineering literature still lacks a comprehensive review of the recent advances of QC. Therefore, this article reviews and summarizes the state of the art to gain insight into how quantum computation can benefit and optimize chemical engineering issues. A bibliography analysis covers the comprehensive literature in QC and analyzes quantum computing research in chemical engineering on various publication topics, using Clarivate analytics covering the years 1990 to 2020. After the bibliographic analysis, relevant applications of QC in chemical and biochemical engineering are highlighted and a conclusion offers an outlook of future directions within the field.

Eda Dolak

The integration of quantum optimization techniques into chemical engineering has emerged as a frontier in achieving more efficient, sustainable, and adaptive process design. This study presents a qualitative investigation into the role of multi-objective quantum optimization frameworks for enhancing process intensification in next-generation chemical plants. The research explores how quantum-inspired algorithms—leveraging quantum superposition, entanglement, and probabilistic search mechanisms—can optimize multiple competing objectives such as energy consumption, production throughput, and environmental impact simultaneously. Through a comprehensive review of both academic and industrial literature, the study identifies key trends in the use of hybrid quantum–classical models for solving high-dimensional optimization problems in chemical plant design and operation. It examines how quantum-assisted methods, including the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), can accelerate simulation convergence and decision-making in process systems engineering. The qualitative findings underscore the transformative potential of quantum-enhanced computation in enabling process intensification, where chemical operations are redesigned for maximum efficiency and minimal ecological footprint. Furthermore, this research analyzes the interdisciplinary interface between quantum computing, chemical engineering, and sustainable manufacturing, emphasizing how algorithmic innovation can support cleaner, more resource-efficient production ecosystems. Conceptual models were developed to illustrate the role of quantum optimization in guiding strategic decisions related to resource allocation, system design, and environmental trade-offs. The study concludes that quantum optimization will likely become a cornerstone of the digital and sustainable transformation of the chemical industry, facilitating intelligent automation and data-driven sustainability in the decades ahead.