DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms Via Diffusion Models
Year: 2026
Type: conference-paper
Abstract: Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization parameters that shape the optimization landscape. Existing machine learning–based initializers achieve state-of-the-art results yet remain constrained to single-task domains and datasets of only hundreds of samples. We address these limitations by reformulating VQA parameter initialization as a generative modeling problem and introducing DiffQ, a parameter initializer based on the Denoising Diffusion Probabilistic Model (DDPM). To support robust training and evaluation, we construct a dataset of 15,085 instances spanning three domains and five representative tasks. Experiments demonstrate that DiffQ surpasses baselines, reducing initial loss by up to 8.95 and convergence steps by up to 23.4%. The code and data used in this paper are available at https://github.com/chizhang24/DiffQ.
Authors: Chi Zhang, Mengxin Zheng, Qian Lou, Fan Chen
Institutions: University of Central Florida, Indiana University Bloomington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415254198