mlsanityData to 14 Oct 2026

DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models

arXiv 2509.17324 ↗First posted 22 Sep 2025cs.ET cs.LG quant-phcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

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 small 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%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 19 of its 31 references that are in the map. First posted on arXiv 22 Sep 2025.

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19 of 31 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Quantum circuit synthesis with diffusion models3 Nov
  2. 2022Classifier-Free Diffusion Guidance26 Jul
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  4. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  5. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Video Diffusion Modelscited by 2,305
  6. 2020DiffWave: A Versatile Diffusion Model for Audio Synthesiscited by 1,354
  7. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  8. 2020Variational Quantum Algorithmscited by 692
  9. 2018Barren plateaus in quantum neural network training landscapescited by 630
  10. 2024Barren Plateaus in Variational Quantum Computingcited by 133
  11. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 98
  12. 2025Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithmscited by 5
2 more
  1. 2022NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithmscited by 4
  2. 2024Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithmcited by 1

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  3. 2025Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits
  4. 2025Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
  5. 2024Mixed-State Quantum Denoising Diffusion Probabilistic Model
  6. 2023Generative quantum machine learning via denoising diffusion probabilistic models
  7. 2025Synthesis of discrete-continuous quantum circuits with multimodal diffusion models
  8. 2026An Hybrid Quantum-Classical Diffusion Model for Image Generation