Quantum-Noise-driven Generative Diffusion Models

by   Marco Parigi, et al.

Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion models are an emerging framework that have recently overcome the performance of the generative adversarial networks in creating synthetic text and high-quality images. Here, we propose and discuss the quantum generalization of diffusion models, i.e., three quantum-noise-driven generative diffusion models that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non-trivial interplay among coherence, entanglement and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, we suggest to exploit quantum noise not as an issue to be detected and solved but instead as a very remarkably beneficial key ingredient to generate much more complex probability distributions that would be difficult or even impossible to express classically, and from which a quantum processor might sample more efficiently than a classical one. Therefore, our results are expected to pave the way for new quantum-inspired or quantum-based generative diffusion algorithms addressing more powerfully classical tasks as data generation/prediction with widespread real-world applications ranging from climate forecasting to neuroscience, from traffic flow analysis to financial forecasting.


Quantum Wasserstein Generative Adversarial Networks

The study of quantum generative models is well-motivated, not only becau...

Enhancing Generative Models via Quantum Correlations

Generative modeling using samples drawn from the probability distributio...

A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models

Generative modeling has seen a rising interest in both classical and qua...

Experimental demonstration of a quantum generative adversarial network for continuous distributions

The potential advantage of machine learning in quantum computers is a to...

Improving Adversarial Robustness by Contrastive Guided Diffusion Process

Synthetic data generation has become an emerging tool to help improve th...

Application of quantum-inspired generative models to small molecular datasets

Quantum and quantum-inspired machine learning has emerged as a promising...

Generative Modeling with Quantum Neurons

The recently proposed Quantum Neuron Born Machine (QNBM) has demonstrate...

Please sign up or login with your details

Forgot password? Click here to reset