How AI helps programming a quantum computer

The method developed at the University of Innsbruck produces quantum circuits based on user specifications and tailored to the features of the quantum hardware the circuit will be run on. Harald Ritsch
University of Innsbruck

Researchers from the University of Innsbruck have unveiled a novel method to prepare quantum operations on a given quantum computer, using a machine learning generative model to find the appropriate sequence of quantum gates to execute a quantum operation. The study, recently published in Nature Machine Intelligence, marks a significant step forward in unleashing the full extent of quantum computing.

Generative models like diffusion models are one of the most important recent developments in Machine Learning (ML), with models as Stable Diffusion and Dall.e revolutionizing the field of image generation. These models are able to produce high quality images based on some text description.

“Our new model for programming quantum computers does the same but, instead of generating images, it generates quantum circuits based on the text description of the quantum operation to be performed”,

explains Gorka Muñoz-Gil from the Department of Theoretical Physics of the University of Innsbruck, Austria.

To prepare a certain quantum state or execute an algorithm on a quantum computer, one needs to find the appropriate sequence of quantum gates to perform such operations. While this is rather easy in classical computing, it is a great challenge in quantum computing, due to the particularities of the quantum…