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12.07.2019 – 14:00

Technische Universität München

Artificial neural network resolves puzzles from condensed matter physics

TECHNICAL UNIVERSITY OF MUNICH

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phone: +49 89 289 10510 - e-mail: presse@tum.de - web: www.tum.de

This text on the web: https://www.tum.de/nc/en/about-tum/news/press-releases/details/35570/

High resolution images: https://mediatum.ub.tum.de/1510324

NEWS RELEASE

Which is the perfect quantum theory?

Artificial neural network resolves puzzles from condensed matter physics

For some phenomena in quantum many-body physics several competing theories exist. But which of them describes a quantum phenomenon best? A team of researchers from the Technical University of Munich (TUM) and Harvard University in the United States has now successfully deployed artificial neural networks for image analysis of quantum systems.

Is that a dog or a cat? Such a classification is a prime example of machine learning: artificial neural networks can be trained to analyze images by looking for patterns that are characteristic of specific objects. Provided the system has learned such patterns, it is able to recognize dogs or cats on any picture.

Using the same principle, neural networks can detect changes in tissue on radiological images. Physicists are now using the method to analyze images - so-called snapshots - of quantum many-body systems and find out which theory describes the observed phenomena best.

The quantum world of probabilities

Several phenomena in condensed matter physics, which studies solids and liquids, remain shrouded in mystery. For example, so far it remains elusive why the electrical resistance of high-temperature superconductors drops to zero at temperatures of about -200 degrees Celsius.

Understanding such extraordinary states of matter is challenging: quantum simulators based on ultracold Lithium atoms have been developed to study the physics of high-temperature superconductors. They take snapshots of the quantum system, which exists simultaneously in different configurations - physicists speak of a superposition. Each snapshot of the quantum system gives one specific configuration according to its quantum mechanical probability.

In order to understand such quantum systems, various theoretical models have been developed. But how well do they reflect reality? The question can be answered by analyzing the image data.

Neural networks investigate the quantum world

To this end, a research team at the Technical University of Munich and at Harvard University has successfully employed machine learning: The researchers trained an artificial neural network to distinguish between two competing theories.

"Similar to the detection of cats or dogs in pictures, images of configurations from every quantum theory are fed into the neural network," says Annabelle Bohrdt, a doctoral student at TUM. "The network parameters are then optimized to give each image the right label - in this case, they are just theory A or theory B instead of cat or dog."

After the training phase with theoretical data, the neural network had to apply what it had learned and assign snapshots from the quantum simulators to theory A or B. The network thus selected the theory which is more predictive.

In the future the researchers plan to use this new method to assess the accuracy of several theoretical descriptions. The aim is to understand the main physical effects of high-temperature superconductivity, which has many important applications, with lossless electric power transmission and efficient magnetic resonance imaging being just two examples.

Publication:

Classifying snapshots of the doped Hubbard model with machine learning

Annabelle Bohrdt, Christie S. Chiu, Geoffrey Ji, Muqing Xu, Daniel Greif, Markus Greiner, Eugene Demler, Fabian Grusdt und Michael Knap

nature physics, July 1, 2019 - DOI: 10.1038/s41567-019-0565-x

https://www.nature.com/articles/s41567-019-0565-x

More information:

The research was funded by the National Science Foundation (NSF), the US Air Force's Office of Scientific Research (AFOSR), the National Defense Science and Engineering Graduate (NDSEG) Program of the US-Department of Defense, the Gordon and Betty Moore Foundation EPIQS program, the Studienstiftung des deutschen Volkes, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) as part of the Cluster of Excellence Munich Center for Quantum Science and Technology (MCQST) and the Transregio TRR80 as well as the TUM Institute for Advanced Study, funded by the German Excellence Initiative and the European Union, where Prof. Knap holds the Rudolf Mößbauer Tenure Track Professorship for Collective Quantum Dynamics.

Links to previous press releases concerning quantum physics and artificial intelligence can be found below the actual press release:

https://www.tum.de/nc/en/about-tum/news/press-releases/details/35570/

High resolution image:

https://mediatum.ub.tum.de/1510324

Contact:

Prof. Dr. Michael Knap

Professorship for Collective Quantum Dynamics

Technical University of Munich

James-Franck Str. 1, 85748 Garching

Tel.: +49 89 289 53777 - E-Mail: michael.knap@ph.tum.de

Web: http://users.ph.tum.de/ga32pex/

The Technical University of Munich (TUM) is one of Europe's leading research
universities, with around 550 professors, 41,000 students, and 10,000 academic
and non-academic staff. Its focus areas are the engineering sciences, natural
sciences, life sciences and medicine, combined with economic and social
sciences. TUM acts as an entrepreneurial university that promotes talents and
creates value for society. In that it profits from having strong partners in
science and industry. It is represented worldwide with the TUM Asia campus in
Singapore as well as offices in Beijing, Brussels, Cairo, Mumbai, San Francisco,
and São Paulo. Nobel Prize winners and inventors such as Rudolf Diesel, Carl von
Linde, and Rudolf Mößbauer have done research at TUM. In 2006 and 2012 it won
recognition as a German "Excellence University." In international rankings, TUM
regularly places among the best universities in Germany. www.tum.de