New superconducting diodes could improve the performance of quantum computers and artificial intelligence
(Nanowerk News) A University of Minnesota Twin Cities-led team has developed a new superconducting diode, a key component in electronic devices, that could help scale quantum computers for industrial use and enhance the performance of artificial intelligence systems. Compared to other superconducting diodes, the researchers’ device is more energy efficient; can process several electrical signals at once; and contains a series of gates to control the flow of energy, a feature that has never before been integrated into a superconducting diode.
This paper is published in Nature Communications (“Gate tunable superconducting diode effect in a three-terminal Josephson device”).
Diodes allow current to flow in one direction but not the other in an electric circuit. It’s basically half of the transistor, the main element in a computer chip. Diodes are usually made with semiconductors, but researchers are interested in making them with superconductors, which have the ability to transfer energy without losing any power along the way.
“We want to make computers more powerful, but there are some hard limits we will soon reach with our current materials and fabrication methods,” said Vlad Pribiag, senior author of the paper and associate professor at the University of Minnesota School of Physics and Astronomy. “We needed new ways to develop computers, and one of the biggest challenges to increasing computing power today is that they waste so much energy. So, we’re thinking about ways that superconducting technology can help with that.”
University of Minnesota researchers created the device using three Josephson junctions, which are created by sandwiching pieces of non-superconducting material between superconductors. In this case, the researchers connected the superconductor with a layer of semiconductor. The device’s unique design allows researchers to use voltage to control device behavior.
Their devices also have the ability to process multiple signal inputs, whereas a typical diode can only handle one input and one output. This feature can be applied in neuromorphic computing, a method of engineering electrical circuits to mimic the way neurons function in the brain to improve the performance of artificial intelligence systems.
“The device we built has the highest energy efficiency ever demonstrated, and for the first time, we’ve shown that you can add a gate and apply an electric field to tune this effect,” explains Mohit Gupta, first author of the paper and Ph.D. student at the University of Minnesota School of Physics and Astronomy. “Other researchers have made superconducting devices before, but the materials they used were very difficult to manufacture. Our designs use more industrial-friendly materials and introduce new functionality.”
The method the researchers used can in principle be used with any type of superconductor, making it more versatile and easier to use than other techniques in the field. Due to these qualities, their devices are more compatible for industrial applications and could help advance the development of quantum computers for wider use.
“Right now, all the quantum computing machines out there are very basic relatively to the needs of real-world applications,” says Pribiag. “Upgrades are necessary to have computers powerful enough to tackle both useful and complex problems. A lot of people are researching algorithms and use cases for computers or AI engines that have the potential to outperform classic computers. Here, we are developing the hardware that will allow quantum computers to implement these algorithms. It shows the power of universities seeding these ideas that eventually make their way to industry and are integrated into a practical machine.”
