Artificial intelligence algorithms are based on mathematical models or neural networks, inspired by the biological structure of the human brain, which is made up of inter­connected nodes. Just as in our brain the learning process is based on the rearrangement of the connections between neurons, artificial neural networks can be trained on a set of known data that modify its internal structure, making it capable of performing “human” tasks, such as face recog­nition, the inter­pretation of medical images to diagnose diseases and even driving a car. For this reason, research is underway, at academic and industrial level, aiming to obtain integrated and compact devices capable of performing the mathe­matical operations required for the operation of neural networks in a rapid and efficient way.

A breakthrough in this field was the discovery of the memory-resistor or memristor, a component that changes its electrical resistance based on a memory of the current that passed through it. Scientists have realized that this functioning is sur­prisingly similar to that of neural synapses, i.e. the connections between neurons in the brain, and the memristor has become a fundamental component with which to build neuro­morphic archi­tectures, that is, forged as a model of our brain. A group of experi­mental physicists led by Roberto Osellame, research director at the Institute of Photonics and Nanotechnologies of the National Research Council (CNR-IFN), and Philip Walther, University of Vienna, in colla­boration with Andrea Crespi, Politecnico di Milano, have shown that it is possible to engineer an optical device with the same functional charac­teristics as the memristor, capable of operating on quantum states of light and thus encoding and trans­mitting quantum information: a quantum memristor.

“Making such a device is no trivial matter, since the dynamics of the memristor tend to compromise certain advantageous aspects of quantum devices. Our researchers have overcome this challenge by employing single photons and exploiting their quantum ability to propagate simul­taneously in two or more paths,” explains Osellame. “These photons are conducted in optical circuits, fabricated by means of laser pulses in a glass chip, dynamically recon­figurable, which can support quantum states of super­position on different paths. By measuring the flow of photons propagating on one of these paths, it is possible, through a complex scheme of electronic feedback, to reconfigure the trans­mission of the device on the other output, and this enables us to obtain a func­tionality equivalent to that of the memristor.”

“We also simulated an entire optical network made up of quantum memristors,” explains Andrea Crespi, “showing that it could be used to learn both classical and quantum tasks”. This result seems to suggest that the quantum memristor may be the missing link between arti­ficial intelli­gence and quantum computing. “Unleashing the potential of quantum resources within arti­ficial intelli­gence appli­cations is one of the greatest challenges of current research, both in quantum physics and in computer science,” concludes Michele Spagnolo, from the University of Vienna. These new results are a step forward towards a future in which quantum arti­ficial intelli­gence will be a reality. (Source: Politec. Milano)

Reference: M. Spagnolo et al.: Experimental photonic quantum memristor, Nat. Phot. 16, 318 (2022); DOI: 10.1038/s41566-022-00973-5

Link: Vienna Center for Quantum Science and Technology (VCQ), University of Vienna, Vienna, Austria