Improving AI performance and quantum computing scalability

Researchers have developed a novel superconducting diode that shows promise for boosting the performance of artificial intelligence systems and scaling quantum computers for industrial applications. This device surpasses its counterparts with superior energy efficiency, the ability to process multiple electrical signals simultaneously, and a unique set of gates that control the flow of energy.

A team of researchers has developed a highly efficient superconducting diode with potential applications for scaling quantum computers and enhancing AI systems. This device can handle multiple signals simultaneously, a feature beneficial for neuromorphic computing, and is made from more industrial-friendly materials, paving the way for broader industrial applications.

A team led by the University of Minnesota Twin Cities has developed a new superconducting diode, a key component in electronic devices that could help scale quantum computers for industrial use and improve the performance of artificial intelligence systems.

Compared to other superconducting diodes, the researchers’ device is more energy-efficient; can process multiple electrical signals simultaneously; and contains a series of gates to control the flow of energy, a function never before incorporated into a superconducting diode.

The article was published in Nature Communications, a peer-reviewed scientific journal dedicated to science and technology.

A diode allows current to flow in one direction in a circuit but not the other. It’s essentially half of a transistor, the main element in computer chips. Diodes are usually made from semiconductors, but researchers are interested in making them from superconductors, which can transfer energy without losing energy.

A team led by the University of Minnesota Twin Cities has developed a more energy-efficient, tunable superconducting diode – a promising component for future electronic devices – that could help scale up industrial quantum computing and improve artificial intelligence systems. Photo credit: Olivia Hultgren / University of Minnesota Twin Cities

“We want to make computers more powerful, but with our current materials and manufacturing methods, we will soon hit some hard limits,” said Vlad Pribiag, the paper’s senior author and an associate professor in the University of Minnesota School of Physics and Astronomy. “We need new ways to design computers, and one of the biggest challenges in increasing computing power right now is that they use so much energy. So we are thinking about how superconducting technologies could help with that.”

The University of Minnesota researchers created the device using three Josephson junctions, which are made by sandwiching pieces of non-superconducting material between superconductors. In this case, the researchers connected the superconductors with layers of semiconductors. The device’s unique design allows researchers to control the behavior of the device using voltage.

Your device is also capable of handling multiple signal inputs, while typical diodes can only handle one input and one output. This feature could find application in neuromorphic computing, a method of constructing electrical circuits to mimic how neurons in the brain work, thereby improving the performance of artificial intelligence systems.

“The device we made has nearly the highest energy efficiency ever demonstrated, and for the first time we have shown that you can add gates and apply electric fields to tune this effect,” said Mohit Gupta, the first author of the paper and Ph .D. Student in the Department of Physics and Astronomy at the University of Minnesota. “Other researchers have already made superconducting devices, but the materials they used were very difficult to make. Our design uses materials that are more industrial friendly and offer new functionalities.”

The method the researchers used can in principle be applied to any type of superconductor, making it more versatile and easier to use than other techniques in the field. These properties make their device more suitable for industrial applications and could help advance the development of quantum computing for wider use.

“At the moment, all available quantum computers are very simple compared to the requirements of real-world applications,” said Pribiag. “To have a computer powerful enough to handle useful, complex problems requires scaling. Many people are researching algorithms and use cases for computers or AI machines that could potentially outperform classical computers. Here we are developing the hardware that could enable quantum computers to implement these algorithms. This shows the power of universities when it comes to driving these ideas forward, which will eventually find their way into industry and be incorporated into practical machines.”

Reference: “Gate-tunable supraconducting diode effect in a three-terminal Josephson device” by Mohit Gupta, Gino V. Graziano, Mihir Pendharkar, Jason T. Dong, Connor P. Dempsey, Chris Palmstrøm, and Vlad S. Pribiag, May 29 2023, Nature Communication.
DOI: 10.1038/s41467-023-38856-0

This research was funded primarily by the US Department of Energy with partial support from Microsoft Research and the National Science Foundation.

In addition to Pribiag and Gupta, the research team also included University of Minnesota School of Physics and Astronomy graduate student Gino Graziano and University of California, Santa Barbara researchers Mihir Pendharkar, Jason Dong, Connor Dempsey, and Chris Palmstrøm.