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

TL;DR

The paper investigates the convergence of AI-based scientific automation and quantum computing within CAE, proposing Quantum CAE as a practical framework for accelerating simulation, optimization, and learning in engineering design. It introduces the 'digital scientist' concept and a six-level automation ladder, arguing Level-3 automation is already feasible through CAE-like workflows. The authors illustrate near-term Quantum CAE with manufacturing-focused case studies using quantum annealing-based combinatorial optimization (BOCS, FMQA), and discuss quantum-algorithm developments for simulations including radiative transfer and Boltzmann-type equations. They discuss advances toward Levels 4–5 via specialized AI agents capable of designing quantum circuits (e.g., GQE) and stress the need for integrated infrastructure and cross-disciplinary collaboration to realize pervasive automated discovery. The work highlights potential near-term gains and outlines future directions for practical quantum-enabled automation in science and engineering.

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.

Figures (4)

• Figure 1: Process of automation in science and product design. Hypotheses are automatically generated based on existing knowledge and validated through experiments or simulations, producing new findings. These findings are integrated into the knowledge base, enabling iterative hypothesis generation. Parentheses indicate specific information processing tasks involved in the automation process. This process parallels design automation workflows such as those found in Computer-Aided Engineering (CAE).
• Figure 2: Optimization results for a noise filter. Similar solutions were obtained using topology optimization (left, reproduced from Maruyama2021 with permission) and black-box optimization (right, reproduced from Okada2023).
• Figure 3: Relationship between the number of data acquisitions (x-axis) and the cost function (y-axis). Feasible solutions emerge as the number of data acquisitions increases. (reproduced from Okada2023)
• Figure 4: Encoder-decoder model for generating quantum circuits to solve optimization problems. The QUBO matrix, which encodes an optimization problem, is first converted into a graph and then processed using a graph neural network as the encoder and a transformer as the decoder. (reproduced from Minami2025 with modifications)