Towards Efficient Quantum Hybrid Diffusion Models
- URL: http://arxiv.org/abs/2402.16147v1
- Date: Sun, 25 Feb 2024 16:57:51 GMT
- Title: Towards Efficient Quantum Hybrid Diffusion Models
- Authors: Francesca De Falco, Andrea Ceschini, Alessandro Sebastianelli,
Bertrand Le Saux, Massimo Panella
- Abstract summary: We propose a new methodology to design quantum hybrid diffusion models.
We propose two possible hybridization schemes combining quantum computing's superior generalization with classical networks' modularity.
- Score: 68.43405413443175
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we propose a new methodology to design quantum hybrid
diffusion models, derived from classical U-Nets with ResNet and Attention
layers. Specifically, we propose two possible different hybridization schemes
combining quantum computing's superior generalization with classical networks'
modularity. In the first one, we acted at the vertex: ResNet convolutional
layers are gradually replaced with variational circuits to create Quantum
ResNet blocks. In the second proposed architecture, we extend the hybridization
to the intermediate level of the encoder, due to its higher sensitivity in the
feature extraction process. In order to conduct an in-depth analysis of the
potential advantages stemming from the integration of quantum layers, images
generated by quantum hybrid diffusion models are compared to those generated by
classical models, and evaluated in terms of several quantitative metrics. The
results demonstrate an advantage in using a hybrid quantum diffusion models, as
they generally synthesize better-quality images and converges faster. Moreover,
they show the additional advantage of having a lower number of parameters to
train compared to the classical one, with a reduction that depends on the
extent to which the vertex is hybridized.
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