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Researchers Propose New Topological Phase of Atomic Matter Hosting ‘Photonic Skyrmions

The field of topology or the study of how surfaces behave in different dimensions has profoundly influenced the current understanding of matter. The prime example is the topological insulator, which conducts electricity only on the surface while being completely insulating inside the bulk. Topological insulators behave like a metal, i.e., silver on the surface, but inside, it would behave like glass. These properties are defined using the conductivity or flow of electrons depicting whether there is a highway or a road-block for their motion. One major driver of future applications for topological insulators is in the field of spin-electronic devices since these electrons spin in unison, all aligned with each other while flowing on the surface.

Trapping Light that Doesn't Bounce off Track for Faster Electronics

Replacing traditional computer chip components with light-based counterparts will eventually make electronic devices faster due to the wide bandwidth of light. A new protective metamaterial "cladding" prevents light from leaking out of the very curvy pathways it would travel in a computer chip.

Nano-Optical Cables for Wiring Up Photonic Circuits

Researchers at the University of Alberta, Canada, have proposed a new approach to confining light at subdiffraction wavelengths, using transparent metamaterials—without creating heat or losing data, and with dramatically reduced crosstalk.

Wenbo Sun wins outstanding graduate researcher award

Wenbo Sun received the Outstanding Graduate Student Research Award in recognition of his excellence in graduate student research. The award was conferred at the Purdue College of Engineering Graduate Education Awards 2024 luncheon on April 17th, . This is awarded to 13 Ph.D. students (2 from ECE)...

Three papers in NeurIPS 2021

There is a long way to go before quantum information (QI) can have a real-world impact on machine learning applications. However, in the short term, QI presents a principled approach to unravel performance bounds and find hidden quantum-classical parallels for widely used machine learning algorithms...

Tensor Rings for Learning Circular Hidden Markov Models

Congratulations to Mohammad Ali Javidian on the selection of his paper titled “Tensor Rings for Learning Circular Hidden Markov Models” which was virtually presented at the second Workshop on Quantum Tensor Networks in Machine Learning.