Quantum circuit synthesis with diffusion models

arXiv:2311.02041v2 Announce Type: replace-cross
Abstract: Quantum computing has recently emerged as a transformative technology. Yet, its promised advantages rely on efficiently translating quantum operations into viable physical realizations. In this work, we use generative machine learning models, specifically denoising diffusion models (DMs), to facilitate this transformation. Leveraging text-conditioning, we steer the model to produce desired quantum operations within gate-based quantum circuits. Notably, DMs allow to sidestep during training the exponential overhead inherent in the classical simulation of quantum dynamics -- …

abstract advantages arxiv computing cs.ai cs.lg denoising diffusion diffusion models generative machine machine learning machine learning models operations quant-ph quantum quantum computing replace synthesis technology text transformation type work