Photonic Integrated-Based Memristor Gives AI Applicability in Quantum Computing | Research & Technology | Mar 2022

VIENNA, March 31, 2022 – By developing a photonic quantum memistor, researchers at the University of Vienna, the National Research Council (CNR), and the Polytechnic University of Milan may have found a way to link artificial intelligence (AI) and quantum computing. . The researchers engineered a device that works the same as a memristor, while encoding and transmitting quantum information and acting on quantum states.

Memristive devices are electrical components that change their resistance depending on the memory of the previous electrical current flowing through them. They are used in neuromorphic architectures such as neural networks because their behavior is similar to that of a neural synapse.


Abstract representation of a neural network that is made of photons and has memory capability potentially related to artificial intelligence. Courtesy of Equinox Graphics, University of Vienna.


Artificial intelligence applications are guided by neural networks that are mathematically trained to mimic human problem-solving. The quantum memristor will improve the speed and efficiency at which neural networks compute information, potentially providing the memory capabilities needed for computations at the quantum level.

The team’s approach exploited the ability of single photons to propagate simultaneously in a superposition of two or more paths. The researchers demonstrated the quantum memistor on an integrated photonic quantum processor operating on single photons. In the experiment, single photons propagated along waveguides that were laser-written on a glass substrate. The laser-written integrated photonic circuits were reconfigurable by means of integrated phase shifters.

The photonic circuit produced memristive dynamics on single-photon states through measurement and classical feedback. The single photons were guided on a superposition of several paths. One path measured the flow of photons traveling through the device. Through a feedback scheme, this measurement controlled the transmission of coherent quantum information, thus achieving memristive behavior.

After characterizing the memristive dynamics of the quantum memristor and tomographically reconstructing its quantum output state, the researchers demonstrated a potential application of the quantum memristor in the framework of quantum machine learning. They designed a memristor-based quantum reservoir computer and tested it numerically on both classical and quantum learning tasks, achieving strong performance with limited physical and computational resources and, most importantly, no architectural change when moving from one type of task to the other.

To the best of the researchers’ knowledge, this is the first experimental demonstration of a quantum memristor. Realizing such a device is especially challenging because the dynamics of a memristor differ from typical quantum behavior.

Artistic representation of a neural network containing optically-interconnected Mach-Zehnder interferometers.  The interferometer is the main component of the quantum memristor.  Courtesy of Equinox Graphics, University of Vienna.


Artistic representation of a neural network containing optically interconnected Mach-Zehnder interferometers. The interferometer is the main component of the quantum memristor. Courtesy of Equinox Graphics, University of Vienna.


The quantum memistor is scalable to larger architectures using integrated quantum photonics. The only limit to larger scalability, according to the researchers, is the single-photon rate.

The device could be enhanced by integrating optical and electronic components in the same chip; the researchers believe this could be done using current semiconductor technology.

The researchers also believe that the frequency at which the quantum memristor operates could be improved using existing technology. For laser-written circuits, high-frequency operations are readily available at the expense of higher-power consumption, and other photonic platforms routinely enable frequencies even in the Gigahertz regime. To take advantage of higher frequencies, however, the photon detection rate would need to be improved. Faster detection rates could be achieved by using customized fast detectors and bright single-photon sources.

The quantum memistor could provide a missing nonlinear element for quantum optical neural networks, providing a link between artificial intelligence and quantum computing.

The research was published in Nature Photonics (www.doi.org/10.1038/s41566-022-00973-5).

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