Quantum Computing -- Strategic Recommendations for the Industry
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909988858888192 |
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| author | Erdmann, Marvin Karch, Lukas Awasthi, Abhishek Jones, Caitlin Isobel Bhardwaj, Pallavi Krellner, Florian Stein, Jonas Linnhoff-Popien, Claudia Kraus, Nico Eder, Peter Braun, Sarah Liu, Tong |
| author_facet | Erdmann, Marvin Karch, Lukas Awasthi, Abhishek Jones, Caitlin Isobel Bhardwaj, Pallavi Krellner, Florian Stein, Jonas Linnhoff-Popien, Claudia Kraus, Nico Eder, Peter Braun, Sarah Liu, Tong |
| contents | This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08578 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum Computing -- Strategic Recommendations for the Industry Erdmann, Marvin Karch, Lukas Awasthi, Abhishek Jones, Caitlin Isobel Bhardwaj, Pallavi Krellner, Florian Stein, Jonas Linnhoff-Popien, Claudia Kraus, Nico Eder, Peter Braun, Sarah Liu, Tong Quantum Physics Emerging Technologies This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior. |
| title | Quantum Computing -- Strategic Recommendations for the Industry |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2601.08578 |