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

arXiv: https://arxiv.org/abs/2505.10012 · HTML: https://mlsanity.com/paper/2505.10012/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 10 of 10)

- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [A quantum-inspired classical algorithm for recommendation systems](https://mlsanity.com/paper/1807.04271/) (2018)
- [Bayesian Optimization of Combinatorial Structures](https://mlsanity.com/paper/1806.08838/) (2018)
- [Application of QUBO solver using black-box optimization to structural design for resonance avoidance](https://mlsanity.com/paper/2204.04906/) (2022)
- [Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver](https://mlsanity.com/paper/2501.16986/) (2025)
- [Quantum-enhanced Markov chain Monte Carlo](https://mlsanity.com/paper/2203.12497/) (2022)
- [Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming](https://mlsanity.com/paper/2204.10579/) (2022)
- [Automating quantum feature map design via large language models](https://mlsanity.com/paper/2504.07396/) (2025)
- [A black-box optimization method with polynomial-based kernels and quadratic-optimization annealing](https://mlsanity.com/paper/2501.04225/) (2025)
- [Designing thermal radiation metamaterials via hybrid adversarial autoencoder and Bayesian optimization](https://mlsanity.com/paper/2205.01063/) (2022)

## Abstract (arXiv)

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.