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Magically reducing errors in quantum computers

Researchers from The University of Osaka develop a method to prepare high-fidelity 'magic states' for use in quantum computers with less overhead and unprecedented accuracy. This breakthrough aims to overcome the significant obstacle of noise in quantum systems, which can ruin computer setups.

SourceThe University of Osaka·JournalPRX Quantum·TypeComputational simulation/modeling·DateJun 19, 2025

CityU scholars unify color systems using prime numbers

Researchers from City University of Hong Kong developed a unified colour system based on prime numbers, called C<sub>235</sub>, which can represent various colours more efficiently than existing systems like RGB and CMYK. The new colour system has potential applications in designing energy-saving LCD systems and colourizing DNA codons.

SourceCity University of Hong Kong·JournalLight Science & Applications·TypeComputational simulation/modeling·DateMar 2, 2023

Efficient coding: How the brain optimizes allocation of resources

A study published in eLife found that rats exhibit efficient coding processes for visual stimuli, similar to those observed in humans. This suggests a universal principle in vision, where the brain adapts to its environment by specializing in the recognition of informative signals, thereby conserving computational resources and energy.

SourceScuola Internazionale Superiore di Studi Avanzati·JournaleLife·TypeExperimental study·DateDec 7, 2021

Living retina achieves sensitivity and efficiency engineers can only dream about

The living retina achieves sensitivity and efficiency that human engineers can only dream about, with optimized mosaics of different sensitivities parsing 40 visual features. The retinas adapt to current conditions and conserve energy by tuning out noise, making them orders of magnitude less power-hungry than smartphone sensors.

SourceDuke University·JournalProceedings of the National Academy of Sciences·TypeData/statistical analysis·DateSep 28, 2021

Unifying the theories of neural information encoding

Researchers at IST Austria and Paris create framework connecting three neural information encoding theories, providing concrete predictions for previously unstudied coding regimes. The unified theory allows neurons to have mixed coding objectives, covering a range of constraints and phenomena not explained by existing models.

SourceInstitute of Science and Technology Austria·JournalProceedings of the National Academy of Sciences·DateDec 20, 2017