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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.

An international research team has used a "thermal metamaterial" to control the emission of radiation at high temperatures, an advance that could bring devices able to efficiently harvest waste heat from power plants and factories.

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.

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.

Zubin Jacob gives invited talk at Rice University quantum seminar “New directions in quantum sensing: Photonic spin density and atomistic topological electrodynamics”. Nitrogen vacancy (NV) centers in diamond have emerged as promising room temperature quantum sensors for probing condensed matter...

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...

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.

A new approach to control forces and interactions between atoms and molecules, such as those employed by geckos to climb vertical surfaces, could bring advances in new materials for developing quantum light sources.

A newly described property related to the "spin" and momentum of light waves suggests potential practical applications in photonic communications and photonic circuits. Scientists already knew that light waves have an electric field that can rotate as they propagate, which is known as the polarization property of light, and that light waves carry momentum in their direction of motion. In new findings, researchers have discovered a "spin-momentum locking," meaning, for example, light waves that spin in a counterclockwise direction can only move forward, and vice versa.

Researchers at Purdue University have created a quantum spin wave for light. This can be a carrier of information for future nanotechnologies but with a unique twist: they only flow in one direction.