The quantum memristor, the missing link linking artificial intelligence and quantum computing?

The potential of artificial intelligence to help overcome the challenges we face is certain, but the limitation of the computing capacities of conventional computers is an obstacle to solving certain problems. Quantum combined with AI may well be the solution. To demonstrate this, physicists from the University of Vienna, the National Research Council (CNR) and the Politecnico di Milano, led by Professor Philip Walther and Dr Roberto Osellame, designed a quantum optical memristor based on photonics integrated. Their study was published in the journal “nature photonics.” »

Artificial neural networks, which simulate the biological structure of the brain, are the basis of artificial intelligence applications. Thanks to deep learning, they gradually become autonomous but for this, they must be trained on large amounts of data. In addition to volume, one of the biggest challenges in AI is the computing power needed to train neural networks.

Thanks to its combinatorial power, the quantum computer could help reduce learning times and processing times for many AI applications.

From classical memristor to quantum memristor

Described for the first time in 1971 by Leon Chua, the memristor (or memristance) is a passive electronic component, just like the capacitor or the resistor. This component, made up of a ferroelectric layer enclosed by two electrodes, is of interest to researchers for its ability to imitate the neurons and synapses of biological brains and its capacity for self-learning.

The first memristors were based on the transfer of ions or atoms in a material under the effect of an electric voltage and a sudden rise in temperature caused by the electric current but consumed a lot of energy. This is why a research team associating the CNRS, Thales and the University of Cambridge, developed in 2017, a new type of memristor based on a quantum property of matter, the tunnel effect, which allows electrons to jump through a very thin layer of electrically polarized ferroelectric material, like a magnet. Several other quantum memristor proposals were made subsequently but all were faced with limited technological functionality.

The study’s quantum optical memristor

This new memristor is based on a laser-written integrated photonic circuit that is fully reconfigurable by means of integrated phase shifters, and is capable of producing memristive dynamics on single-photon states through a classical measurement and feedback scheme.

The researchers therefore used single photons, or single quantum particles of light, which propagate along laser-etched waveguides on a glass substrate and are guided through an overlay of multiple pathways. One of the latter is used to measure the flux of photons passing through the device and this quantity, through an electronic feedback scheme, modulates the transmission on the other output, thus obtaining the desired memristive behavior.

On the other hand, researchers have developed simulations demonstrating that optical networks containing quantum memristors can be used to learn about both classical and quantum tasks, implying that the quantum memristor could be the missing link connecting the artificial intelligence and quantum computing. Michele Spagnolo, lead author of the paper, concludes:

“Unlocking the full potential of quantum resources within artificial intelligence is one of the greatest challenges in current research in quantum physics and computer science. »

Philip Walther’s group, which co-led this study, also demonstrated that robots can learn faster when they use quantum resources and borrow patterns from quantum computing.

Sources of the article: “Experimental photonic quantum memristor”

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The quantum memristor, the missing link linking artificial intelligence and quantum computing?


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