Next-Gen Superconducting Diode: Enhancing AI Performance and Quantum Computing Scalability

A team of researchers from the University of Minnesota Twin Cities has crafted an advanced superconducting diode. This new invention offers significant potential in improving artificial intelligence (AI) systems and facilitating the scalability of quantum computers for commercial applications. The device excels beyond its equivalents with greater energy efficiency, the capacity to process numerous electrical signals at once, and an innovative sequence of gates for managing energy flow.

The newly designed superconducting diode can process multiple signals at once, a characteristic that is advantageous for neuromorphic computing. Furthermore, the device is constructed with more industry-compatible materials, which broadens the possibilities for larger scale industrial applications.

Compared to other superconducting diodes, the device crafted by the researchers is superior in energy efficiency, can process multiple electrical signals concurrently, and contains an innovative series of gates that control energy transmission, a feature not previously incorporated in a superconducting diode.

The details of this discovery were published in Nature Communications, a peer-reviewed scientific journal focused on natural sciences and engineering. 

Typically, a diode permits current to flow in one direction but prevents it in the other within an electrical circuit. It forms half of a transistor, the central element in computer chips. While diodes are traditionally created with semiconductors, the idea of fabricating them with superconductors, which can transmit energy without power loss, is gaining traction.

The innovative superconducting diode, created by the team led by the University of Minnesota Twin Cities, is not only more energy-efficient and tunable but also a promising component for future electronic devices. This invention could facilitate the scaling up of quantum computers for industry use and elevate the performance of AI systems. (Image credit: Olivia Hultgren / University of Minnesota Twin Cities)

“We are exploring ways to make computers more powerful. However, with our current materials and manufacturing techniques, we are soon going to encounter hard limits,” stated Vlad Pribiag, senior author of the paper and an associate professor at the University of Minnesota School of Physics and Astronomy. “One of the major hurdles for amplifying computing power currently is their high energy dissipation. Hence, we are considering how superconducting technologies could assist in overcoming this issue.”

The researchers at the University of Minnesota constructed the device using three Josephson junctions. These are formed by inserting non-superconducting material pieces between superconductors. In this instance, the researchers connected the superconductors using semiconductor layers, which gave the device its unique design and allowed voltage control over the device’s behavior.

The newly developed device can process multiple signal inputs, a feature not commonly found in standard diodes. This feature could prove valuable in neuromorphic computing, which engineers electrical circuits to replicate neural functionality in the brain to improve AI systems.

Mohit Gupta, the paper’s first author and a Ph.D. student at the University of Minnesota School of Physics and Astronomy, stated, “The device we have created is close to the highest energy efficiency ever demonstrated, and for the first time, we have shown that you can add gates and apply electric fields to adjust this effect. Previous researchers have created superconducting devices, but the materials they used were challenging to fabricate. Our design employs more industry-friendly materials and introduces new functionalities.”

The researchers’ method could theoretically be applied with any type of superconductor, making it more versatile and user-friendly than other techniques in this field. Due to these characteristics, their device is more suited for industrial applications and could facilitate the scaling up of quantum computers for broader use.

Pribiag adds, “Currently, all quantum computing machines are rather basic relative to the needs of practical applications. To develop a computer powerful enough to handle complex, useful problems, scaling up is necessary. Many are researching algorithms and usage cases for computers or AI machines that could potentially surpass classical computers. Here, we’re developing the hardware that could allow quantum computers to implement these algorithms. This demonstrates the significance of universities seeding ideas that eventually make their way to industry and get integrated into practical machines.”

The researchers credited in the paper include, apart from Pribiag and Gupta, graduate student Gino Graziano from the University of Minnesota School of Physics and Astronomy, and researchers from the University of California, Santa Barbara – Mihir Pendharkar, Jason Dong, Connor Dempsey, and Chris Palmstrøm.

The study was majorly funded by the United States Department of Energy, with additional support from Microsoft Research and the National Science Foundation.

Reference: “Gate-tunable superconducting 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, 29 May 2023, Nature Communications. DOI: 10.1038/s41467-023-38856-0

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More about Superconducting Diode for Quantum Computing and AI Enhancement

• University of Minnesota Twin Cities
• Superconducting Diode
• Neuromorphic Computing
• Quantum Computing
• Artificial Intelligence
• Nature Communications
• United States Department of Energy
• Microsoft Research
• National Science Foundation