Quantum Computing for Automotive Applications
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912288205701120 |
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| author | Riofrío, Carlos A. Klepsch, Johannes Finžgar, Jernej Rudi Kiwit, Florian Hölscher, Leonhard Erdmann, Marvin Müller, Lukas Kumar, Chandan Berrada, Youssef Achari Luckow, Andre |
| author_facet | Riofrío, Carlos A. Klepsch, Johannes Finžgar, Jernej Rudi Kiwit, Florian Hölscher, Leonhard Erdmann, Marvin Müller, Lukas Kumar, Chandan Berrada, Youssef Achari Luckow, Andre |
| contents | Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engineering, being particularly promising. This chapter investigates state-of-the-art quantum algorithms to enhance efficiency, accuracy, and scalability across the automotive value chain. We explore recent advances in quantum optimization, machine learning, and numerical and chemistry simulations, highlighting their potential and limitations. We identify and discuss key challenges in near-term and fault-tolerant algorithms and their practical use in industrial applications. While quantum algorithms show potential in many application domains, current noisy intermediate-scale quantum hardware limits scale and, thus, business benefits. In the long term, fault-tolerant systems promise theoretical speedups; however, they also require further progress in hardware and software (e.g., related to error correction and data loading). We expect that with this progress, significant practical benefits will emerge eventually. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14183 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Computing for Automotive Applications Riofrío, Carlos A. Klepsch, Johannes Finžgar, Jernej Rudi Kiwit, Florian Hölscher, Leonhard Erdmann, Marvin Müller, Lukas Kumar, Chandan Berrada, Youssef Achari Luckow, Andre Quantum Physics Emerging Technologies Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engineering, being particularly promising. This chapter investigates state-of-the-art quantum algorithms to enhance efficiency, accuracy, and scalability across the automotive value chain. We explore recent advances in quantum optimization, machine learning, and numerical and chemistry simulations, highlighting their potential and limitations. We identify and discuss key challenges in near-term and fault-tolerant algorithms and their practical use in industrial applications. While quantum algorithms show potential in many application domains, current noisy intermediate-scale quantum hardware limits scale and, thus, business benefits. In the long term, fault-tolerant systems promise theoretical speedups; however, they also require further progress in hardware and software (e.g., related to error correction and data loading). We expect that with this progress, significant practical benefits will emerge eventually. |
| title | Quantum Computing for Automotive Applications |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2409.14183 |