qdiffusion-time-series

name

qdiffusion-time-series

description

Quantum generative diffusion model for time series synthesis (QDiffusion-TS). First quantum diffusion model validated on real quantum hardware (IQM processor). Replaces feed-forward components with quantum neural networks, reducing parameters by ~3 orders of magnitude. Reduces Wasserstein distance by ~44% vs classical. Use when: quantum generative modeling, time series synthesis, quantum-enhanced data augmentation, parameter-efficient diffusion models.

Core Methodology

QDiffusion-TS Architecture

Extends classical diffusion architecture by replacing feed-forward components within the denoising transformer with Quantum Neural Networks (QNNs), yielding a hybrid quantum transformer.

Key Innovation: Parameter Reduction

• Reduces trainable parameters in each replaced component by nearly 3 orders of magnitude
• Maintains or exceeds classical performance with substantially fewer parameters

Performance Metrics

• Wasserstein distance: ~44% reduction vs classical counterpart
• Downstream forecasting: up to 71% improvement in RMSE with synthetic data augmentation
• Validated on financial time series (Apple, Amazon) on IQM quantum processor

Pipeline

Real Time Series → Diffusion Process (noisy) 
                → Denoising Transformer with QNN layers 
                → Synthetic Time Series 
                → Downstream Task (e.g., forecasting)

Quantum-Classical Hybrid Design

• Quantum layers handle the core denoising transformation
• Classical layers handle preprocessing and postprocessing
• Joint training of quantum and classical components

Data Augmentation Strategy

Generated synthetic data is used to augment real training data, improving downstream model performance significantly (up to 71% RMSE improvement).

Implementation Notes

• Requires access to quantum hardware (IQM processor or similar)
• Hybrid architecture allows training with classical simulation, then deployment on quantum hardware
• Particularly effective for small datasets where quantum expressivity provides advantage

Activation

quantum diffusion, quantum generative model, time series synthesis, QDiffusion, quantum data augmentation, hybrid quantum transformer, quantum ML time series