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Quantum Computing in Operations Research

Author

Listed:
  • Stefan Creemers

    (IESEG School of Management, Lille, France KU Leuven, ORSTAT, Leuven, Belgium)

  • Perez Armas Luis Fernando

    (IESEG School of Management, Lille, France)

Abstract

Quantum computing has sparked a tremendous interest from governments, academics, and the private sector alike. According to a 2021 McKinsey report, governments have announced to invest almost $30 billion to develop quantum technologies. Companies such as IBM, Google, Amazon, and Microsoft are also investing heavily, and have already launched commercial quantum-computing services. Research on quantum computing is also on the rise with hundreds of publications in Nature, Science, and PNAS. Despite this enormous interest, quantum computing has received little or no attention in the OR community. This is somewhat surprising given the potential that has been ascribed to quantum computers to solve Operations Research (OR) problems. To investigate the potential of quantum computing from an OR perspective, we discuss the most important quantum algorithms, and use them to effectively solve the knapsack problem for the very first time. We verify our results using Qiskit (IBM’s software development kit for quantum computing), and make available templates that allow to solve other OR problems. In addition, we highlight a number of important limitations and drawbacks of quantum computing (when compared to classical computing), and conclude that quantum computing indeed shows promise, albeit not for every application.

Suggested Citation

  • Stefan Creemers & Perez Armas Luis Fernando, 2022. "Quantum Computing in Operations Research," Working Papers 2022-OPS-01, IESEG School of Management.
  • Handle: RePEc:ies:wpaper:e202208
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    More about this item

    Keywords

    Quantum; computing; algorithm; knapsack;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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