What if computers could think faster while using a fraction of the energy? That future may already be in the lab.

In this episode of NSF Discover Superconducting, we take a deep dive into a talk by Dr. Kevin Osborn of the Joint Quantum Institute at the University of Maryland — exploring ballistic and reversible superconducting logic, a radical rethinking of how digital gates work at the quantum level.

As AI data centers push toward 20–30 megawatt power loads, Osborn's research couldn't be more timely. His team is building logic gates that harness the momentum of fluxons — magnetic flux quanta — to process information without constant power input. The result? Simulations showing over 97% energy efficiency and a potential path to zeptojoule-level computing.

We cover the physics behind Long Josephson Junctions, the Ballistic Flip-Flop (BFF), two-polarity bit systems using fluxons and anti-fluxons, and a theoretical framework for sub-nanosecond qubit readout. Plus: early experimental results from the MIT Lincoln Labs fabrication process.

Perfect for students and researchers in quantum computing, electrical engineering, and sustainable technology.

In this episode, we explore the future of superconductor electronics—a promising post-CMOS computing paradigm offering ultra-low energy consumption and ultra-high processing speeds. Dr. Sasan Razmkhah of USC’s SPORT Lab joins the conversation to discuss cutting-edge research aimed at making superconductor very-large-scale integration (sVLSI) a practical reality.

We dive into the development of Fast-Phase Logic (FPL), a next-generation logic family that uses π-Josephson junctions and stacked zero-Josephson junctions to dramatically increase logic density while reducing power requirements. The discussion also covers hybrid system architectures that integrate superconductor logic with CMOS, spanning multiple temperature zones to balance performance, efficiency, and manufacturability.

Together, these advances outline a roadmap toward scalable, energy-efficient computing systems—pointing to a future where superconductors play a central role in high-performance and AI-driven computing.

This deep-dive episode explores the ideas from Jeff Shainline’s recent seminar on superconducting optoelectronic networks (SOENs) and the radical rethink of AI hardware they represent. We unpack how SOENs combine analog superconducting circuits, integrated memory, and single-photon optical communication to bypass the von Neumann bottleneck and deliver massive gains in speed, energy efficiency, and scalability. If you’re curious about brain-inspired hardware, trillion-parameter AI, or the future of computation, this episode guides you through the key concepts and why they matter.