Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering

TL;DR

This paper explores how the integration of quantum computing and AI can revolutionize scientific and engineering automation, introducing Quantum CAE and discussing its practical applications and future implications.

Contribution

It introduces the Quantum CAE framework that combines quantum algorithms with AI for advanced simulation, optimization, and machine learning in engineering design.

Findings

Quantum CAE enables efficient solving of complex optimization problems.

Case studies demonstrate practical applications of quantum algorithms in engineering.

The paper discusses future automation levels and human-AI-quantum collaboration challenges.

Abstract

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.