On May 15, 2025, Tadashi Kadowaki introduced "Quantum Computer-Aided Engineering" (Quantum CAE) in a groundbreaking paper, proposing a fusion of quantum computing and artificial intelligence to transform automation in science and engineering.

Kadowaki's work presents Quantum CAE as a novel framework that utilizes quantum algorithms for simulation, optimization, and machine learning within engineering design. Through case studies on combinatorial optimization problems, the paper illustrates practical applications of this integration, highlighting the potential for accelerated innovation and efficiency in various industries.

Quantum CAE leverages quantum computing's capabilities to enhance traditional Computer-Aided Engineering processes, aiming to solve complex engineering problems more efficiently. This integration is particularly impactful for combinatorial optimization problems, which are prevalent in various engineering disciplines.

Tadashi Kadowaki is a prominent figure in the field of quantum computing. In 1998, as a postgraduate student at the Tokyo Institute of Technology, he co-authored a seminal paper with Professor Hidetoshi Nishimori, introducing quantum annealing as a method for solving combinatorial optimization problems. This work laid the foundation for practical applications of quantum computing in optimization tasks.

After his academic pursuits, Kadowaki contributed to various industries, including semiconductor development and bioinformatics. In May 2018, he joined DENSO Corporation, a leading automotive components manufacturer, to explore quantum computing applications in the automotive industry. His diverse background underscores his interdisciplinary approach to quantum computing and its applications.

The paper presents practical implementations of Quantum CAE in combinatorial optimization, demonstrating its effectiveness in solving complex engineering challenges. It discusses specialized AI agents proficient in quantum algorithm design, emphasizing their critical role in advancing automation levels. The study examines the interplay among human scientists, AI systems, and quantum computational resources, highlighting a collaborative approach to innovation.

"Quantum CAE represents a transformative future for automated discovery and innovation through the convergence of quantum computing and AI," Kadowaki stated in his paper. He further noted, "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."

The societal implications of Quantum CAE are profound. Enhanced automation in scientific and engineering processes can lead to accelerated innovation cycles, reduced time-to-market for new technologies, and more efficient resource utilization. Industries such as automotive, aerospace, pharmaceuticals, and materials science stand to benefit significantly from these advancements.

Kadowaki's recent work aligns with a broader trend of integrating quantum computing with AI to tackle complex problems. For instance, in January 2025, he co-authored a paper introducing a conditional Generative Quantum Eigensolver (conditional-GQE), a context-aware quantum circuit generator powered by an encoder-decoder Transformer. This approach demonstrated near-perfect performance on combinatorial optimization problems with up to 10 qubits, highlighting the potential of quantum-enhanced AI in solving real-world challenges.

Additionally, in June 2023, Kadowaki proposed an algorithm designed to enhance the performance of quantum annealing within the digital-analog quantum computing (DAQC) framework. This method employs a quantum gate to estimate the quality of the final annealing state, aiming to find the ground state of combinatorial optimization problems more efficiently.

The introduction of Quantum CAE marks a pivotal moment in the evolution of engineering design, offering a glimpse into a future where quantum computing and AI converge to drive unprecedented levels of automation and innovation.