Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915287981359104 |
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| author | Kadowaki, Tadashi |
| author_facet | Kadowaki, Tadashi |
| contents | Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodologies. This perspective highlights parallels between scientific automation and established Computer-Aided Engineering (CAE) practices, introducing Quantum CAE as a framework that leverages quantum algorithms for simulation, optimization, and machine learning within engineering design. Practical implementations of Quantum CAE are illustrated through case studies for combinatorial optimization problems. Further discussions include advancements toward higher automation levels, highlighting the critical role of specialized AI agents proficient in quantum algorithm design. The integration of quantum computing with AI raises significant questions about the collaborative dynamics among human scientists and engineers, AI systems, and quantum computational resources, underscoring a transformative future for automated discovery and innovation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering Kadowaki, Tadashi Quantum Physics Artificial Intelligence Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodologies. This perspective highlights parallels between scientific automation and established Computer-Aided Engineering (CAE) practices, introducing Quantum CAE as a framework that leverages quantum algorithms for simulation, optimization, and machine learning within engineering design. Practical implementations of Quantum CAE are illustrated through case studies for combinatorial optimization problems. Further discussions include advancements toward higher automation levels, highlighting the critical role of specialized AI agents proficient in quantum algorithm design. The integration of quantum computing with AI raises significant questions about the collaborative dynamics among human scientists and engineers, AI systems, and quantum computational resources, underscoring a transformative future for automated discovery and innovation. |
| title | Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10012 |