QDiffusion-TS is the first quantum generative diffusion model for time series, achieving ~44% lower Wasserstein distance on Apple and Amazon stock data and up to 71% better forecasting RMSE with ~1000x fewer parameters than classical diffusion.
Quantum diffusion models
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
abstract
We propose a quantum version of a generative diffusion model. In this algorithm, artificial neural networks are replaced with parameterized quantum circuits, in order to directly generate quantum states. We present both a full quantum and a latent quantum version of the algorithm; we also present a conditioned version of these models. The models' performances have been evaluated using quantitative metrics complemented by qualitative assessments. An implementation of a simplified version of the algorithm has been executed on real NISQ quantum hardware.
representative citing papers
Measurement-based quantum diffusion models are introduced to recover pure and mixed quantum states via weak measurements, quantum score matching, and Petz recovery maps with error bounds, bridging to classical stochastic reversals.
SQGen maps a quantized tensor-train image model with latent-modulated re-uploading rotations onto a shallow native quantum circuit that generates images without a classical decoder.
A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.
citing papers explorer
-
Quantum Generative Diffusion Model for Real-World Time Series
QDiffusion-TS is the first quantum generative diffusion model for time series, achieving ~44% lower Wasserstein distance on Apple and Amazon stock data and up to 71% better forecasting RMSE with ~1000x fewer parameters than classical diffusion.
-
Measurement-Based Quantum Diffusion Models
Measurement-based quantum diffusion models are introduced to recover pure and mixed quantum states via weak measurements, quantum score matching, and Petz recovery maps with error bounds, bridging to classical stochastic reversals.
-
SQGen: Structured Quantum Image Generation with Latent-Modulated Quantized Tensor Trains
SQGen maps a quantized tensor-train image model with latent-modulated re-uploading rotations onto a shallow native quantum circuit that generates images without a classical decoder.
-
Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks
A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.