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Chemical Reviews

Quantum Algorithms and Applications for Open Quantum Systems

Luis H. Delgado-Granados, Timothy J. Krogmeier, LeeAnn M. Sager-Smith, Irma Avdic, Zixuan Hu, Manas Sajjan, Maryam Abbasi, Scott E. Smart, Prineha Narang, Sabre Kais, Anthony W. Schlimgen, Kade Head-Marsden, David A. Mazziotti

Published Feb 4, 2025• 33 citations
Publisher

Abstract

Accurate models for open quantum systems─quantum states that have nontrivial interactions with their environment─may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.

DOI
10.1021/acs.chemrev.4c00428

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Accurate models for open quantum systems─quantum states that have nontrivial interactions with their environment─may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.
Accurate models for open quantum systems -- quantum states that have non-trivial interactions with their environment -- may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.
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Abstract Quantum computers are designed to outperform standard computers by running quantum algorithms. Areas in which quantum algorithms can be applied include cryptography, search and optimisation, simulation of quantum systems and solving large systems of linear equations. Here we briefly survey some known quantum algorithms, with an emphasis on a broad overview of their applications rather than their technical details. We include a discussion of recent developments and near-term applications of quantum algorithms.
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Quantum computing plays a significant role in simulating molecules and atoms and offers advantages in chemistry over classical computing. The potential of Quantum Machine Learning (QML) can be used in drug discovery, chemical reaction simulations, and Material design for pharmaceuticals. QML leverages quantum computing and advanced machine learning to accelerate the identification of drug candidates, predict molecular interactions, and optimize compounds. In this paper, we present a systematic review of the methods used for molecular property prediction and molecular generation using quantum machine learning. We have included the recent research, perspective, advantages, and challenges that must be addressed to achieve this task. The objective of this research is not only to discuss current strategies and methods used for drug discovery but also to promote interdisciplinary research in the field of quantum computing and chemistry for wellness.
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