Quantum computing for chemical and biomolecular product design

Andersson, Martin; Jones, Mark Nicholas; Mikkelsen, Kurt V.; You, Fengqi; Mansouri, Seyed Soheil

doi:10.1016/j.coche.2021.100754

Cited by 36 publications

(16 citation statements)

References 57 publications

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“…Research is progressing, and some early experiments are using QC to address chemical engineering problems on model systems, 22–24 as well as estimating resource requirements for full‐scale applications 25–27 . Chemical and biomolecular product design may also benefit from QC‐assisted computational tools for applications, including quantum chemical property calculations, molecular reaction dynamics, and others 28 . At the same time, the growing interest in QC carries some hype and confusion about its potential and present‐day status 15,29 .…”

Section: Introductionmentioning

confidence: 99%

“…[25][26][27] Chemical and biomolecular product design may also benefit from QC-assisted computational tools for applications, including quantum chemical property calculations, molecular reaction dynamics, and others. 28 At the same time, the growing interest in QC carries some hype and confusion about its potential and present-day status. 15,29 All of this motivates this perspectives article.…”

Section: Introductionmentioning

confidence: 99%

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Perspectives of quantum computing for chemical engineering

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Perspectives of quantum computing for chemical engineering

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“…Solution techniques based on mathematical programming may demonstrate exponential time complexity for complex scheduling problems, while the heuristic methods allow for faster solution times at the expense of optimality. QC can potentially improve the solution times for optimization problems arising in various domains ranging from the energy sector [16] to chemical product design [17]. Therefore, to overcome the shortcomings of classical solution approaches, the development of novel quantum solution approaches is required.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling

Ajagekar

Hamoud

You

2022

IEEE Trans. Quantum Eng.

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Quantum computing (QC) holds great promise to open up a new era of computing and has been receiving significant attention recently. To overcome the performance limitations of near-term QC, utilizing the current quantum computers to complement classical techniques for solving real-world problems is of utmost importance. In this paper, we develop QC-based solution strategies that exploit quantum annealing and classical optimization techniques for solving large-scale scheduling problems in manufacturing systems. The applications of the proposed algorithms are illustrated through two case studies in production scheduling. First, we present a hybrid QC-based solution approach for the job-shop scheduling problem. Second, we propose a hybrid QC-based parametric method for the multi-purpose batch scheduling problem with a fractional objective. The proposed hybrid algorithms can tackle optimization problems formulated as mixed-integer linear and mixed-integer fractional programs, respectively, and provide feasibility guarantees. Performance comparison between state-of-the-art exact and heuristic solvers and the proposed QC-based hybrid solution techniques is presented for both job-shop and batch scheduling problems. Unlike conventional classical solution techniques, the proposed hybrid frameworks harness quantum annealing to supplement established deterministic optimization algorithms and demonstrate performance efficiency over standard off-the-shelf optimization solvers.

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“…Quantum computing hardware is quickly developing [1] together with the theory of quantum computing algorithms [2]. Problems historically solved classically have been translated into quantum computing formulations [3][4][5][6], and these formulations differ even within the various quantum hardware types. In this work, we explore how well constrained optimization can be implemented between two well-known quantum computing hardware types, (i) adiabatic, and (ii) the gate-based.…”

Section: Introductionmentioning

confidence: 99%

Quantum Computing Approaches for Mission Covering Optimization

et al. 2022

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Quantum computing has the potential to revolutionize the way hard computational problems are solved in terms of speed and accuracy. Quantum hardware is an active area of research and different hardware platforms are being developed. Quantum algorithms target each hardware implementation and bring advantages to specific applications. The focus of this paper is to compare how well quantum annealing techniques and the QAOA models constrained optimization problems. As a use case, a constrained optimization problem called mission covering optimization is used. Quantum annealing is implemented in adiabatic hardware such as D-Wave, and QAOA is implemented in gate-based hardware such as IBM. This effort provides results in terms of cost, timing, constraints held, and qubits used.

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Quantum computing for chemical and biomolecular product design

Cited by 36 publications

References 57 publications

Perspectives of quantum computing for chemical engineering

Perspectives of quantum computing for chemical engineering

Hybrid Classical-Quantum Optimization Techniques for Solving Mixed-Integer Programming Problems in Production Scheduling

Quantum Computing Approaches for Mission Covering Optimization

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