Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering
It addresses the integration of quantum computing and AI for automation in science and engineering, but is incremental as it builds on existing CAE practices.
This perspective introduces Quantum CAE as a framework that uses quantum algorithms for simulation, optimization, and machine learning in engineering design, illustrated through case studies for combinatorial optimization problems, but does not provide concrete numerical results.
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.