Which is the perfect quantum theory?

Which is the perfect quantum theory?
A team of researchers from the Technical University of Munich and Harvard University in the United States has successfully deployed artificial neural networks for image analysis of quantum systems. They analyze snapshots of a quantum system, which exists simultaneously in different configurations. Each snapshot represents one specific configuration according to its quantum mechanical probability. By assigning the snapshots to one of two theories the neural network can determine which theory which is more predictive. Credit: Annabelle Bohrdt and Christoph Hohmann/MCQST

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: 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 many-body systems and find out which 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 .

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 had to apply what it had learned and assign snapshots from the quantum simulators to theory A or B. The 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.


Explore further

Simulating quantum systems with neural networks

More information: Annabelle Bohrdt et al, Classifying snapshots of the doped Hubbard model with machine learning, Nature Physics (2019). DOI: 10.1038/s41567-019-0565-x
Journal information: Nature Physics

Citation: Which is the perfect quantum theory? (2019, July 12) retrieved 24 July 2019 from https://phys.org/news/2019-07-quantum-theory.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
116 shares

Feedback to editors

User comments

Jul 12, 2019
Spinning sphere theory describes quantum interactions best. It starts with a single sphere and builds up to a multiverse made of multiverses. The best clue is to start with predicting the gravitational constant. Please see, "Predicting the Gravitational Constant from the New Physics of a Rotating Universe" From there one begins to realize that the big bang did not happen at one time, but instead the outer edges of the universe are colliding at relativistic speeds all the time, creating the products of what we not observe as the big bang.

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more