AI Agents for Variational Quantum Circuit Design

TL;DR

This paper presents an autonomous agent-based framework for designing variational quantum circuits, enabling scalable, automated exploration of circuit architectures to improve quantum machine learning performance.

Contribution

The paper introduces a novel agent-driven approach for automated VQC architecture search, integrating high-level reasoning with quantum simulation for improved design efficiency.

Findings

Agent successfully evolves circuit architectures towards higher expressiveness.

Automated framework reduces manual effort in VQC design.

Progressively improves task performance with minimal human intervention.

Abstract

Variational quantum circuits (VQCs) constitute a central building block of near-term quantum machine learning (QML), yet the principled design of expressive and trainable architectures remains a major open challenge. The VQC design space grows combinatorially with the number of qubits, layers, entanglement structures, and gate parameterizations, rendering manual circuit construction inefficient and often suboptimal. We introduce an autonomous agent-based framework for VQC architecture search that integrates high-level reasoning with a quantum simulation environment. The agent proposes candidate circuit architectures, evaluates them through fully automated training and validation pipelines, and iteratively improves its design strategy via performance-driven feedback. Empirically, we show that the agent autonomously evolves circuit architectures from simple initial ans\"atze toward increasingly expressive designs, progressively trying to improve task performance. This demonstrates that agentic AI can effectively navigate and refine the VQC design landscape with minimal human intervention, providing a scalable methodology for automated quantum model development in the Noisy Intermediate-Scale Quantum (NISQ) regime.