LLM-Guided Ansätze Design for Quantum Circuit Born Machines in Financial Generative Modeling
This work addresses the problem of improving quantum generative modeling for financial applications, such as modeling Japanese government bond interest rates, by making QCBM ansätze more practical on near-term quantum devices, representing an incremental advance in quantum architecture search.
The authors tackled the challenge of designing expressive and hardware-efficient ansätze for quantum circuit Born machines (QCBMs) on NISQ devices by introducing a prompt-based framework using large language models (LLMs) to generate hardware-aware architectures, resulting in significantly shallower circuits with superior generative performance compared to a standard baseline on real IBM quantum hardware with 12 qubits.
Quantum generative modeling using quantum circuit Born machines (QCBMs) shows promising potential for practical quantum advantage. However, discovering ansätze that are both expressive and hardware-efficient remains a key challenge, particularly on noisy intermediate-scale quantum (NISQ) devices. In this work, we introduce a prompt-based framework that leverages large language models (LLMs) to generate hardware-aware QCBM architectures. Prompts are conditioned on qubit connectivity, gate error rates, and hardware topology, while iterative feedback, including Kullback-Leibler (KL) divergence, circuit depth, and validity, is used to refine the circuits. We evaluate our method on a financial modeling task involving daily changes in Japanese government bond (JGB) interest rates. Our results show that the LLM-generated ansätze are significantly shallower and achieve superior generative performance compared to the standard baseline when executed on real IBM quantum hardware using 12 qubits. These findings demonstrate the practical utility of LLM-driven quantum architecture search and highlight a promising path toward robust, deployable generative models for near-term quantum devices.