Leveraging Diffusion Models for Parameterized Quantum Circuit Generation

TL;DR

This paper introduces a diffusion model-based generative approach for creating parameterized quantum circuits, enabling efficient synthesis of circuit architectures and parameters to improve quantum state generation and machine learning tasks.

Contribution

It extends diffusion models to conditionally generate parameterized quantum circuits, demonstrating versatility and efficiency in quantum circuit design.

Findings

Effective synthesis of PQCs for GHZ states

High accuracy in quantum machine learning tasks

Strong generalization across gate sets and qubit counts

Abstract

Quantum computing holds immense potential, yet its practical success depends on multiple factors, including advances in quantum circuit design. In this paper, we introduce a generative approach based on denoising diffusion models (DMs) to synthesize parameterized quantum circuits (PQCs). Extending the recent diffusion model pipeline of F\"urrutter et al. [1], our model effectively conditions the synthesis process, enabling the simultaneous generation of circuit architectures and their continuous gate parameters. We demonstrate our approach in synthesizing PQCs optimized for generating high-fidelity Greenberger-Horne-Zeilinger (GHZ) states and achieving high accuracy in quantum machine learning (QML) classification tasks. Our results indicate a strong generalization across varying gate sets and scaling qubit counts, highlighting the versatility and computational efficiency of diffusion-based methods. This work illustrates the potential of generative models as a powerful tool for accelerating and optimizing the design of PQCs, supporting the development of more practical and scalable quantum applications.