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Cell Reports Physical Science

Quantum computing and chemistry

Jared D. Weidman, Manas Sajjan, Camille A. Mikolas, Zachary J. Stewart, J. Pollanen, Sabre Kais, Angela K. Wilson

Published Jul 15, 2024• 42 citations
Publisher

Abstract

As the year-to-year gains in speeds of classical computers continue to taper off, computational chemists are increasingly examining quantum computing as a possible route to achieve greater computational performance. Quantum computers, built upon the properties of superposition, interference, and entanglement of quantum bits, offer, in principle, the possibility to outperform classical computers for solving many important classes of problems. In the field of chemistry, quantum algorithm development offers promising propositions for solving classically intractable problems in areas such as electronic structure, chemical quantum dynamics, spectroscopy, and cheminformatics. However, physical implementations of quantum computers are still in their infancy and have yet to outperform classical computers for useful computations. Still, quantum software development for chemistry is a highly active area of research. In this perspective, we summarize recent progress in the areas of quantum computing algorithms, hardware, and software, and we describe the challenges that remain for useful implementations of quantum computing for chemical applications.

DOI
10.1016/j.xcrp.2024.102105

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As the year-to-year gains in speeds of classical computers continue to taper off, computational chemists are increasingly examining quantum computing as a possible route to achieve greater computational performance. Quantum computers, built upon the properties of superposition, interference, and entanglement of quantum bits, offer, in principle, the possibility to outperform classical computers for solving many important classes of problems. In the field of chemistry, quantum algorithm development offers promising propositions for solving classically intractable problems in areas such as electronic structure, chemical quantum dynamics, spectroscopy, and cheminformatics. However, physical implementations of quantum computers are still in their infancy and have yet to outperform classical computers for useful computations. Still, quantum software development for chemistry is a highly active area of research. In this perspective, we summarize recent progress in the areas of quantum computing algorithms, hardware, and software, and we describe the challenges that remain for useful implementations of quantum computing for chemical applications.
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.
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.
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.
Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.
Abstract Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.
As we begin to reach the limits of classical computing, quantum computing has emerged as a technology that has captured the imagination of the scientific world. While for many years, the ability to execute quantum algorithms was only a theoretical possibility, recent advances in hardware mean that quantum computing devices now exist that can carry out quantum computation on a limited scale. Thus, it is now a real possibility, and of central importance at this time, to assess the potential impact of quantum computers on real problems of interest. One of the earliest and most compelling applications for quantum computers is Feynman's idea of simulating quantum systems with many degrees of freedom. Such systems are found across chemistry, physics, and materials science. The particular way in which quantum computing extends classical computing means that one cannot expect arbitrary simulations to be sped up by a quantum computer, thus one must carefully identify areas where quantum advantage may be achieved. In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics that are of potential interest for solution on a quantum computer. We then take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal-state simulation and analyze their strengths and weaknesses for future developments.