Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Quantum Machine Learning Guided Image Synthesis

Version 1 : Received: 27 September 2023 / Approved: 28 September 2023 / Online: 29 September 2023 (14:02:07 CEST)

A peer-reviewed article of this Preprint also exists.

Jain, S.; Geraci, J.; Ruda, H.E. Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis. Technologies 2023, 11, 183. Jain, S.; Geraci, J.; Ruda, H.E. Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis. Technologies 2023, 11, 183.

Abstract

Image synthesis poses a challenging problem that researchers in computer vision and machine learning have been grappling with for several decades. Numerous machine learning techniques have emerged and proven effective in generating high-fidelity artificial images. This study breaks new ground by exploring image synthesis through generative learning using the D-Wave 2000Q quantum annealer, marking the first attempt to address the issue of generative image synthesis on a Quantum Processing Unit (QPU). Alongside executing image synthesis on the quantum annealer, this research also compares its performance with existing classical models and delves into resolving the Generative Learning Trilemma.

Keywords

quantum algorithms; quantum machine learning; generative ai; quantum generative ai; generative learning; diffusion ai

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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