Artificial neurons go quantum with photonic circuits
Photonic quantum memristor scheme. Credit: Nature Photonics (2022). DOI: 10.1038/s41566-022-00973-5

Artificial intelligence has become ubiquitous with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven to be capable of more power than the world's largest computer. Physicists at the University of Vienna have demonstrated a new device, called the quantum memristor, which may allow us to combine these two worlds. The experiment was carried out in collaboration with the National Research Council and the Politecnico di Milano in Italy and has been realized on an integrated quantum processor. The work is in the current issue of a journal.

Neural networks are mathematical models used in artificial intelligence applications. The models are inspired by the human brain. Neural networks can be trained by tuning their internal structure until they are capable of human-level tasks, such as recognizing our face, interpreting medical images for diagnosis, and even driving our cars. It has become a major research focus to have integrated devices that are capable of performing computations in neural networks quickly and efficiently.

The memristor was discovered in 2008 and is one of the major game-changing discoveries in the field. The name memory-resistor, or memristor, refers to the device that changes its resistance depending on a memory of the past current. Scientists realized that the peculiar behavior of memristors was similar to that of neural synapses after discovering it. The memristor has become a fundamental building block.

A group of experimental physicists from the University of Vienna, the National Research Council and the Politecnico di Milano have shown that it is possible to engineer a device that has the same behavior as a me. A quantum memristor is what it is. The dynamics of a memristor can affect quantum behavior.

Physicists have overcome the challenge by using single quantum particles of lights and exploiting their unique ability to travel in a superposition of two or more paths. In their experiment, single photons travel along a series of paths and are guided on a series of paths. One of these paths is used to measure the amount of light going through the device, and this quantity through a complex electronic feedback scheme, thus achieving the desired memristive behavior.

Simulations show that optical networks with the quantum memristor can be used to learn both classical and quantum tasks, suggesting that the missing link between artificial intelligence and quantum computing is the quantum memristor.

One of the greatest challenges of the current research in quantum physics and computer science is the unlocked full potential of quantum resources within artificial intelligence. The group of Philip Walther of the University of Vienna has recently demonstrated that robots can learn faster by using quantum resources. A future where quantum artificial intelligence becomes reality is represented by this new achievement.

More information: Michele Spagnolo, Experimental photonic quantum memristor, Nature Photonics (2022). DOI: 10.1038/s41566-022-00973-5. Journal information: Nature Photonics Citation: Artificial neurons go quantum with photonic circuits (2022, March 24) retrieved 24 March 2022 from 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.