Nav: Home

Which is the perfect quantum theory?

July 12, 2019

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.
-end-
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.

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

Technical University of Munich (TUM)

Related Quantum Articles:

Quantum leap: Photon discovery is a major step toward at-scale quantum technologies
A team of physicists at the University of Bristol has developed the first integrated photon source with the potential to deliver large-scale quantum photonics.
USTC realizes the first quantum-entangling-measurements-enhanced quantum orienteering
Researchers enhanced the performance of quantum orienteering with entangling measurements via photonic quantum walks.
A convex-optimization-based quantum process tomography method for reconstructing quantum channels
Researchers from SJTU have developed a convex-optimization-based quantum process tomography method for reconstructing quantum channels, and have shown the validity to seawater channels and general channels, enabling a more precise and robust estimation of the elements of the process matrix with less demands on preliminary resources.
A quantum of solid
Researchers in Austria use lasers to levitate and cool a glass nanoparticle into the quantum regime.
What a pair! Coupled quantum dots may offer a new way to store quantum information
Researchers at the National Institute of Standards and Technology (NIST) and their colleagues have for the first time created and imaged a novel pair of quantum dots -- tiny islands of confined electric charge that act like interacting artificial atoms.
Quantum physics: On the way to quantum networks
Physicists at Ludwig-Maximilians-Universitaet (LMU) in Munich, together with colleagues at Saarland University, have successfully demonstrated the transport of an entangled state between an atom and a photon via an optic fiber over a distance of up to 20 km -- thus setting a new record.
How we learn is a quantum-like manner!
It brings people new perspectives on understanding how human brains run.
How sensitive can a quantum detector be?
Measuring the energy of quantum states requires detecting energy changes so exceptionally small they are hard to pick out from background fluctuations, like using only a thermometer to try and work out if someone has blown out a candle in the room you're in.
Spinning quantum dots
A new paper in EPJ B presents a theoretical analysis of electron spins in moving semiconductor quantum dots, showing how these can be controlled by electric fields in a way that suggests they may be usable as information storage and processing components of quantum computers.
In leap for quantum computing, silicon quantum bits establish a long-distance relationship
In an important step forward in the quest to build a quantum computer using silicon-based hardware, researchers at Princeton have succeeded in making possible the exchange of information between two qubits located relatively far apart -- about the length of a grain of rice, which is a considerable distance on a computer chip.
More Quantum News and Quantum Current Events

Trending Science News

Current Coronavirus (COVID-19) News

Top Science Podcasts

We have hand picked the top science podcasts of 2020.
Now Playing: TED Radio Hour

Processing The Pandemic
Between the pandemic and America's reckoning with racism and police brutality, many of us are anxious, angry, and depressed. This hour, TED Fellow and writer Laurel Braitman helps us process it all.
Now Playing: Science for the People

#568 Poker Face Psychology
Anyone who's seen pop culture depictions of poker might think statistics and math is the only way to get ahead. But no, there's psychology too. Author Maria Konnikova took her Ph.D. in psychology to the poker table, and turned out to be good. So good, she went pro in poker, and learned all about her own biases on the way. We're talking about her new book "The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win".
Now Playing: Radiolab

Invisible Allies
As scientists have been scrambling to find new and better ways to treat covid-19, they've come across some unexpected allies. Invisible and primordial, these protectors have been with us all along. And they just might help us to better weather this viral storm. To kick things off, we travel through time from a homeless shelter to a military hospital, pondering the pandemic-fighting power of the sun. And then, we dive deep into the periodic table to look at how a simple element might actually be a microbe's biggest foe. This episode was reported by Simon Adler and Molly Webster, and produced by Annie McEwen and Pat Walters. Support Radiolab today at Radiolab.org/donate.