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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.

Topological Edge Plasmons in Quantum Many-Body Systems by Prof. Stephan Haas

Prof. Stephan Haas discussed how the many-body excitation spectrum of topological insulators is affected by the presence of long-range Coulomb interactions. In the one-dimensional Su-Schrieffer-Heeger model and its mirror-symmetric variant, strongly localized plasmonic excitations are observed which...

Quantum analog of the maximum power transfer theorem

In our recent work in Optics Express, we discover the quantum analog of the well-known classical maximum power transfer theorem, typically used in classical circuit design. By developing a unified framework, from the Lindblad master equation and describing power delivery in dissipative quantum...

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.