By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Generative models like diffusion model s are one of the most important recent developments in Machine Learning (ML), wi th models as Stable Diffusion and Dall.e revolutionizing the field of image gene ration. 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 d escription of the quantum operation to be performed”, explains Gorka Munoz-Gil f rom the Department of Theoretical Physics of the University of Innsbruck, Austri a. 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 op erations. While this is rather easy in classical computing, it is a great challe nge in quantum computing, due to the particularities of the quantum world. Recen tly, many scientists have proposed methods to build quantum circuits with many r elying machine learning methods. However, training of these ML models is often v ery hard due to the necessity of simulating quantum circuits as the machine lear ns. Diffusion models avoid such problems due to the way how they are trained. “T his provides a tremendous advantage”, explains Gorka Munoz-Gil, who developed th e novel method together with Hans J. Briegel and Florian Furrutter. “Moreover, w e show that denoising diffusion models are accurate in their generation and also very flexible, allowing to generate circuits with different numbers of qubits, as well as types and numbers of quantum gates.” The models also can be tailored to prepare circuits that take into consideration the connectivity of the quantum hardware, i.e. how qubits are connected in the quantum computer. “As producing new circuits is very cheap once the model is trained, one can use it to discover new insights about quantum operations of interest”, Gorka Munoz-Gil names anoth er potential of the new method.
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