Chemical process design has for long been benefiting from computer-aided methods and tools to develop new processes and services that can meet the needs of society. Chemical and biomolecular product design could also benefit from the use of computer-aided solution strategies and computational power to efficiently solve the problems at various scales as the complexity and size of problems grow. In this context, new modes of computation such as quantum computing are receiving increasing attention. While quantum computing has been in development for quite some time, the development of the technology to the point of making commercial use of such resources is quite recent, and still quite limited in scope. However, projections point to a rapid development of quantum computing resources becoming available to academia and industry, which opens potential application areas in chemical and biomolecular product design. With the advent of hybrid algorithms that are able to take advantage of both classical computing and quantum computing resources, as quantum computing grows, more and more problems relevant for chemical product design will become solvable. In this paper, some perspectives are given by identifying a set of needs and challenges for a selected set of opportunities, such as quantum chemistry-based property prediction, protein folding, complex multi-step chemical reactions, and molecular reaction dynamics.
Chemical process design has for long been benefiting from computer-aided methods and tools to develop new processes and services that can meet the needs of society. Chemical and biomolecular product design could also benefit from the use of computer-aided solution strategies and computational power to efficiently solve the problems at various scales as the complexity and size of problems grow. In this context, new modes of computation such as quantum computing are receiving increasing attention. While quantum computing has been in development for quite some time, the development of the technology to the point of making commercial use of such resources is quite recent, and still quite limited in scope. However, projections point to a rapid development of quantum computing resources becoming available to academia and industry, which opens potential application areas in chemical and biomolecular product design. With the advent of hybrid algorithms that are able to take advantage of both classical computing and quantum computing resources, as quantum computing grows, more and more problems relevant for chemical product design will become solvable. In this paper, some perspectives are given by identifying a set of needs and challenges for a selected set of opportunities, such as quantum chemistry-based property prediction, protein folding, complex multi-step chemical reactions, and molecular reaction dynamics.
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.
Quantum chemistry plays a crucial role in advancing fundamental understanding of chemical processes and driving innovation in energy, medicine, and materials science. Recent progress in quantum computing has opened new possibilities for molecular simulations that are beyond the practical limits of classical approaches. Academic studies have demonstrated hybrid Density Functional Theory (DFT) and Variational Quantum Eigensolver (VQE) benchmarks on small transition-metal systems, such as iron porphyrin and heme analogues, using current noisy intermediate-scale quantum (NISQ) hardware. Although full-scale simulations of complex biological systems such as the complete Cytochrome P450 (CYP450) active site remain a long-term goal due to limitations in qubit numbers, coherence times, and error correction, the underlying methodologies are now well established. At present, quantum chemistry applications in drug discovery remain largely experimental, and widespread practical medical impact is expected to require further technological advances over the next 5–10 years. Nevertheless, quantum software and algorithm development for chemical applications is progressing rapidly. This perspective summarizes recent advances in quantum computing algorithms, hardware, and software relevant to chemistry, and critically discusses the remaining challenges and opportunities for applying quantum computing to chemical problems, with particular emphasis on drug discovery and development.
The pharmaceutical industry relies heavily on the drug discovery process to develop new medications that can help treat various diseases. This process is highly crucial but also very time-consuming, as it involves screening a massive number of chemical compounds to identify those that can be developed into effective treatments. Traditional computing methods are often inadequate for this task due to the complexity and vastness of the data involved. Quantum computing is an emerging field that has gained significant attention in recent years due to its ability to perform complex computations at a much faster pace than classical computers. Quantum computing uses the principles of quantum mechanics, which differ significantly from classical computing. Drug discovery involves a process of identifying new drugs with the desired biological activity to treat specific diseases. This process can be quite lengthy, time-consuming, and expensive, often taking many years to develop a single drug. However, quantum computing has the potential to revolutionize the drug discovery process. Its ability to perform complex calculations quickly and efficiently could significantly speed up the drug discovery process, making it more cost-effective and accurate. Quantum computing's potential in drug discovery requires further research to fully understand and develop practical applications. The use of quantum computing requires the development of specialized algorithms and software tailored to quantum computing. Further investment in research and development is needed to fully leverage the potential of this technology. This paper explores the potential of quantum computing in drug discovery. We will analyze the benefits of using quantum computing in drug discovery, including faster and more accurate data processing, improved optimization, and more efficient screening of chemical compounds. Our paper will also discuss the challenges and limitations that need to be addressed to fully leverage the potential of quantum computing in drug discovery. This will include discussing potential applications of quantum computing in drug discovery, as well as the need for further investment in research and development to fully realize the potential of this technology. Overall, our paper will provide a comprehensive analysis of the potential of quantum computing in drug discovery, exploring both the benefits and challenges associated with this technology and providing recommendations for future research and development.
Numerous reports claim that quantum advantage, which should emerge as a direct consequence of the advent of quantum computers, will herald a new era of chemical research because it will enable scientists to perform the kinds of quantum chemical simulations that have not been possible before. Such simulations on quantum computers, promising a significantly greater accuracy and speed, are projected to exert a great impact on the way we can probe reality, predict the outcomes of chemical experiments, and even drive design of drugs, catalysts, and materials. In this work we review the current status of quantum hardware and algorithm theory and examine whether such popular claims about quantum advantage are really going to be transformative. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules. In particular, we directly compare the resources and timings associated with classical and quantum computers for the molecules H2 for increasing basis set sizes, and Cr2 for a variety of complete active spaces (CAS) within the scope of the CASCI and CASSCF methods. The results obtained for the chromium dimer enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than analogous computations on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.
Quantum computing is emerging as a transformative approach in bioinformatics and quantum chemistry, addressing fundamental computational limitations of classical methods in molecular science. Many biological and chemical systems are inherently governed by quantum mechanics, making accurate simulation with classical algorithms computationally expensive or infeasible. This review examines how quantum computing, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, is advancing molecular modeling, reaction dynamics, and bioinformatics applications. Key quantum algorithms, including the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE), demonstrate increasing capability in approximating molecular energies and simulating quantum systems. In parallel, applications in bioinformatics—such as protein folding, genomic search, and combinatorial optimization—highlight the potential of quantum computing to address complex biological problems. Quantum machine learning further extends these capabilities by integrating quantum algorithms with data-driven modeling. However, current progress is constrained by NISQ-era limitations, including noise, limited qubit counts, and scalability challenges. These constraints highlight the need for hybrid quantum–classical approaches and improved error mitigation strategies. Overall, quantum computing represents a complementary computational paradigm that can enhance classical methods in bioinformatics and chemistry. Continued advances in quantum algorithms, hardware, and hybrid modeling frameworks are expected to expand its role in molecular science and computational biology.
The development and use of quantum computers has the potential for revolutionary impact on the waycomputing supports chemical applications. Quantum computation for chemical applications has received enormous attention in recent years with rapid progress in both theoretical and experimental fronts. The development of quantum algorithms for quantum chemistry is stimulated by the greater availability of more capable quantum computing devices. But major challenges remain with one key example the development of quantum algorithms for solving chemical problems thought to be intractable for classical computers. Other challenges include understanding the role of quantum entanglement, coherence, and superposition in complex chemical processes such as photosynthesis.Theoretical chemists have encountered and analyzed quantum effects in physical chemistry for decades.Combining the results of the chemical physics community with insights from the quantum informationcommunity can lead to a fresh understanding of important chemical processes and the emergence of thenovel many-body formulations amenable for quantum computing. This Research Topic will invite leading scientists from all over the world who work in the fields of quantum information and computation to report their recent research results.