Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models
Year: 2025
Type: preprint
Abstract: We present a novel approach for improving the design of ansatzes in Quantum Generative Adversarial Networks (qGANs) by leveraging Large Language Models (LLMs). By combining the strengths of LLMs with qGANs, our approach iteratively refines ansatz structures to improve accuracy while reducing circuit depth and the number of parameters. This study paves the way for further exploration in AI-driven quantum algorithm design. The flexibility of our proposed workflow extends to other quantum variational algorithms, providing a general framework for optimizing quantum circuits in a variety of quantum computing tasks.
Source: arXiv (Cornell University)
Authors: Ueda, Kento, Matsuo, Atsushi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6891855764