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Diamonds are for quantum sensing

A team of researchers at the University of Tsukuba has developed a new method for measuring tiny changes in magnetic fields using nitrogen-vacancy defects in diamonds. This breakthrough could lead to more accurate quantum sensors and spintronic computers, enabling precise monitoring of temperature, magnetic, and electric fields.

SourceUniversity of Tsukuba·JournalAPL Photonics·DateJun 16, 2022

A novel all-optical switching method makes optical computing and communication systems more power-efficient

A novel all-optical switching method has been developed to make optical computing and communication systems more power-efficient. The method utilizes the quantum optical phenomenon of Enhancement of Index of Refraction (EIR) to achieve ultrafast switching times, ultralow threshold control power, and high switching efficiency.

SourceTampere University·JournalNature Communications·TypeExperimental study·DateJun 6, 2022

Research team makes breakthrough discovery in light interactions with nanoparticles, paving the way for advances in optical computing

A research team at CUNY ASRC made a breakthrough discovery in nanomaterials and light-wave interactions that enables small, low-energy optical computers capable of advanced computing. The discovery demonstrates unprecedented speeds and nearly zero energy demands for solving complex mathematical problems.

SourceAdvanced Science Research Center, GC/CUNY·JournalPhysical Review Letters·TypeComputational simulation/modeling·DateFeb 25, 2022

Optically generated quantum fluids of light reveal exotic matter-wave states in condensed matter physics

Scientists from Skoltech and the University of Southampton created an all-optical lattice that houses polaritons, quasiparticles with half-light and half-matter properties. They demonstrated breakthrough results for condensed matter physics and flatband engineering.

SourceSkolkovo Institute of Science and Technology (Skoltech)·JournalNature Communications·TypeExperimental study·DateSep 30, 2021

Diffractive networks light the way for optical image classification

Researchers at UCLA have developed Diffractive Deep Neural Networks (D2NNs) for all-optical object classification, achieving higher accuracy than individual constituent D2NNs and digital AI models. The success of the ensemble learning approach demonstrates the power of combining multiple predictions to obtain a more accurate prediction.

Accelerating AI computing to the speed of light

A team of researchers has developed an optical computing core prototype using phase-change material, accelerating neural networks and reducing energy consumption for AI applications. The technology is scalable and directly applicable to cloud computing, making it a promising solution for the growing demands of AI online.

SourceUniversity of Washington·JournalNature Communications·DateJan 8, 2021

Dueling dipoles

A new theory of energy transfer in photosynthesis is being developed based on experimental findings that challenge the traditional dipole-based mechanism. Energy is rapidly and efficiently transferred when dipoles are orthogonally disposed, contrary to previous assumptions.

SourceLudwig-Maximilians-Universität München·JournalJournal of the American Chemical Society·DateDec 7, 2010