Quickly finding exotic topological phases is vital for new, ultra-powerful computers. Finding the insulating phase in this research is only the beginning for this technique.
The technique connects neural networks to the theory of the quantum world. The quantum world often leads to incredible properties that could unleash powerful, energy-efficient electronics.
This technique gives scientists the tools to find and map other exotic phases faster. There is growing interest in harnessing machine learning to answer questions about the physics of condensed matter, like metals and insulators, including how to understand the interactions of many electrons.
Quantum systems can have exponentially large parameter spaces similar to big data sets of images or analysis of consumer data. Therefore, machine-learning algorithms based on neural networks could also be trained to identify quantum phases.
Training on so much information is hard. However, the relevant information is much smaller. Read more from phys.org…
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