Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866929761884831744 |
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| author | Ueda, Kento Matsuo, Atsushi |
| author_facet | Ueda, Kento Matsuo, Atsushi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models Ueda, Kento Matsuo, Atsushi Quantum Physics 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. |
| title | Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2503.12884 |