Scalable Quantum State Preparation via Large-Language-Model-Driven Discovery

TL;DR

This paper presents an AI-assisted framework using large language models to design scalable quantum circuits for state preparation in complex quantum field theories, demonstrating success in 1+1d and 2+1d models.

Contribution

It introduces a novel LLM-driven approach for discovering compact, scalable quantum circuits, enabling efficient state preparation in high-dimensional quantum systems.

Findings

Discovered a 4-parameter circuit for 1+1d XY spin chain with sub-percent energy deviation.

Extended the framework to 2+1d scalar field theories, finding a size-independent, 3-parameter ansatz.

Validated the approach on the Zuchongzhi quantum processor.

Abstract

Efficient quantum state preparation remains a central challenge in first-principles quantum simulations of dynamics in quantum field theories, where the Hilbert space is intrinsically infinite-dimensional. Here, we introduce a large language model (LLM)-assisted framework for quantum-circuit design that systematically scales state-preparation circuits to large lattice volumes. Applied to a 1+1d XY spin chain, the LLM autonomously discovers a compact 4-parameter circuit that captures boundary-induced symmetry breaking with sub-percent energy deviation, enabling successful validation on the \texttt{Zuchongzhi} quantum processor. Guided by this insight, we extend the framework to 2+1d quantum field theories, where scalable variational ans\"atze have remained elusive. For a scalar field theory, the search yields a symmetry-preserving, 3-parameter shallow-depth ansatz whose optimized parameters converge to size-independent constants for lattices $n \ge 4$, providing, to our knowledge, the first scalable ansatz for this class of 2+1d models. Our results establish a practical route toward AI-assisted, human-guided discovery in quantum simulation.