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Optimizing Ansatz Design in Quantum Generative Adversarial Networks Using Large Language Models

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arxiv 2503.12884 v1 pith:NJJJVDUH submitted 2025-03-17 quant-ph

Kento Ueda , Atsushi Matsuo This is my paper · ORCID
classification quant-ph
keywords quantumdesignadversarialansatzapproachgenerativelanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Circuit Generation via test-time learning with large language models

    quant-ph 2026-02 conditional novelty 5.0 of 10

    An LLM with memory, score feedback, and restart-from-best finds high-entanglement quantum circuits, reaching Meyer-Wallach 1.0 on 25 qubits within 45 queries.

  2. LLM-Guided Ans\"atze Design for Quantum Circuit Born Machines in Financial Generative Modeling

    quant-ph 2025-09 conditional novelty 4.0 of 10

    LLM-generated quantum circuit Born machine ansätze, guided by hardware-aware prompts and iterative KL feedback, outperform a standard deep ansatz on 12-qubit IBM hardware for Japanese bond-rate modeling.

  3. Towards quantum machine learning for assessing the resilience of post-quantum cryptography

    quant-ph 2026-07 conditional novelty 3.0 of 10

    A 16-qubit QGAN can approximate the first-byte distribution of SPHINCS+ signatures in simulation, but the result is a small-scale, unbenchmarked demonstration with no attack.

  4. A Survey of Quantum Generative Adversarial Networks: Architectures, Use Cases, and Real-World Implementations

    quant-ph 2025-06 conditional

    A review that maps QGAN architectures, use cases, and hardware demonstrations, with special focus on work from 2023 onward.

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