Researchers at the University of Innsbruck have developed a new model that uses machine learning generative models to program quantum computers to find the exact sequence of quantum gates to perform quantum operations.This study was recently published in the journal nature machine intelligence.
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One of the most important advances in machine learning (ML) in recent years has been the use of generative models such as Dall.e and diffusion models such as stable diffusion, which have completely changed the field of image production. Based on the text description, these models can produce images of excellent quality.
A new model for programming quantum computers does the same thing, but instead of generating images, it generates quantum circuits based on textual descriptions of the quantum operations to be performed..
Gorka Muñoz Gil, Department of Theoretical Physics, University of Innsbruck
To prepare a specific quantum state or run an algorithm on a quantum computer, it is necessary to find a suitable quantum gate sequence. Due to the peculiarities of the quantum environment, this, while very simple in classical computing, becomes a critical issue in quantum computing.
Recently, many scientists have proposed strategies for creating quantum circuits, many of which rely on machine learning techniques.
However, training these machine learning models is often difficult because they require simulating quantum circuits during the learning process. Diffusion models are trained to avoid such problems.
This provides a tremendous advantage. Furthermore, we have shown that the denoising diffusion model is both accurate in its generation and highly flexible, allowing the generation of circuits that differ not only in the types and numbers of qubits, but also in the types and numbers of quantum gates..
Gorka Muñoz Gil, Department of Theoretical Physics, University of Innsbruck
Additionally, the model can be customized to build circuits that take into account the connectivity of quantum hardware, or the connections of qubits within a quantum computer.
“Once the model is trained, creating new circuits is very cheap and can be used to discover new insights into the quantum operations of interest.. ”
Gorka Muñoz Gil, Department of Theoretical Physics, University of Innsbruck
Gil developed this method in collaboration with Hans J. Briegel and Florian Fürrutter.
The University of Innsbruck's approach creates quantum circuits that are customized to the characteristics of the quantum hardware on which they operate, based on the user's specifications. This is a major advance in harnessing the full potential of quantum computing.
This research was funded by the Austrian Science Fund FWF and the European Union, among others.
Reference magazines:
Frutter, F. others. (2024) Quantum circuit synthesis using a diffusion model. nature machine intelligence. doi.org/10.1038/s42256-024-00831-9
Source: https://www.uibk.ac.at/en/
