Introducing quantum convolutional neural networks

Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics ...

Holography and criticality in matchgate tensor networks

Tensor networks take a central role in quantum physics as they can provide an efficient approximation to specific classes of quantum states. The associated graphical language can also easily describe and pictorially reason ...

Novel networking

As a new joint appointee at the U.S. Department of Energy's Brookhaven National Laboratory, Eden Figueroa is getting accustomed to traversing between his roles within the Lab's Computational Science Initiative (CSI) and Instrumentation ...

Technologies for the sixth generation cellular network

Future wireless data networks will have to reach higher transmission rates and shorter delays, while supplying an increasing number of end devices. For this purpose, network structures consisting of many small radio cells ...

Which is the perfect quantum theory?

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 ...

Simulating quantum systems with neural networks

Even on the scale of everyday life, nature is governed by the laws of quantum physics. These laws explain common phenomena like light, sound, heat, or even the trajectories of balls on a pool table. But when applied to a ...

Machine learning unlocks mysteries of quantum physics

Understanding electrons' intricate behavior has led to discoveries that transformed society, such as the revolution in computing made possible by the invention of the transistor.

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