by Michael Hallermayer.
This is the first time that a theoretical prediction in quantum physics has been confirmed. Even for supercomputers, the calculations for this are too complex. The researchers used machine learning methods to simplify them. Understanding fundamental principles of the quantum world is improved by the study. It has been published.
It is easy to calculate the motion of a single ball. Predicting the trajectory of gas particles in a vessel that are constantly colliding and being slowed down is much more difficult. What if it's not clear how fast each particle is moving, so that they can have many different velocities at any given time?
In the same way, quantum mechanical particles can have all possible properties at the same time. The state space of quantum mechanical systems is huge. You have to consider the complete state spaces of quantum particles if you want to make a realistic simulation.
Prof. Dr. Heyl is a professor at the University of Augsburg. The number of particles increases the amount of work that needs to be done. It is so large that even the fastest supercomputers can't handle it. This is a big challenge of quantum physics.
Neural networks help with the problem.
Artificial neural networks were used to simplify the problem. The quantum mechanical state can be changed. Heyl says it's manageable for computers.
The scientists used this method to investigate an important theoretical prediction that has remained an outstanding challenge so far. A quantum phase transition is a description of the behavior of physical systems. There is a phase transition from water to ice. A magnet can be demagnetized at high temperatures.
The magnet starts to form again if you cool the material the other way around. This doesn't happen uniformly across the whole material. Many small magnets with different poles are created at the same time. The result is a mosaic of smaller magnets. Physicists claim that it has defects.
In other words, how many mini-magnets the material will eventually be composed of, is predicted by the Kibble-Zurek mechanism. The number of defects is universal and independent of the details in the microscope. Even if the composition of the material is completely different, it behaves exactly the same.
The Kibble-Zurek mechanism was involved in the creation of the universe.
The creation of structure in the universe was explained by the Kibble- Zurek mechanism. The hosted matter was distributed evenly after the big bang. It has been a mystery for a long time how a group of objects could form out of such a state.
The Kibble-Zurek mechanism gives an explanation in this case. The defects were similar to magnets as the universe cooled down. The processes in the world are understood. There is one type of phase transition for which it is not possible to verify the validity of the mechanism. Heyl says that they only exist at a zero temperature point. The phase transition doesn't happen during cooling but through changes in the interaction energy.
The scientists have created a simulation of a quantum phase transition on a computer. For the first time, they were able to show that the Kibble-Zurek mechanism works in the quantum world as well. The physicist says that it wasn't an obvious conclusion. We can better describe the dynamics of quantum mechanical systems of many particles thanks to our study.
More information: Markus Schmitt et al, Quantum phase transition dynamics in the two-dimensional transverse-field Ising model, Science Advances (2022). DOI: 10.1126/sciadv.abl6850 Journal information: Science Advances Provided by Universität Augsburg Citation: Researchers answer fundamental question of quantum physics (2022, September 22) retrieved 22 September 2022 from https://phys.org/news/2022-09-fundamental-quantum-physics.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.
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