Automating quantum feature map design via large language models

📰 ArXiv cs.AI

Large language models can automate the design of quantum feature maps, a key component of quantum machine learning

advanced Published 25 Mar 2026

Action Steps

Utilize large language models to generate initial quantum feature map designs
Evaluate the generated designs using quantum machine learning metrics
Refine the designs using feedback from the evaluation process
Iterate on the design process to achieve optimal performance

Who Needs to Know This

Quantum machine learning researchers and engineers can benefit from this automation, as it can speed up the design process and improve the performance of quantum feature maps

Key Insight

💡 Large language models can be used to automate the design of quantum feature maps, potentially leading to more efficient and effective quantum machine learning

Key Takeaways

Large language models can automate the design of quantum feature maps, a key component of quantum machine learning

Full Article

Title: Automating quantum feature map design via large language models

Abstract:
arXiv:2504.07396v2 Announce Type: replace-cross Abstract: Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, designing quantum feature maps that offer practical advantages over classical methods remains an open challenge. In this work, we propose an agentic system that autonomously generates, evaluates, and refines quantum feature m

Read full paper → ← Back to Reads