Lifeboat Foundation

Reliable quantum gates are the fundamental component of quantum information processing. However, achieving high-dimensional unitary transformations in a scalable and compact manner with ultrahigh fidelities remains a great challenge.

To address this issue, scientists in China showcase the use of deep diffractive neural networks (D²NNs) to construct a series of high-dimensional quantum gates, which are encoded by the spatial modes of photons. This work, published in Light: Science & Applications, offers a new paradigm for quantum gate design using deep learning.

Quantum computing holds the promise of transforming our information processing methodologies, and at its core, reliable quantum logic gates play an essential role in quantum information processing.

A LK99 researcher from Hubei, China, said that his paper might not be released before the Lunar New Year because of patent issues, announced the main findings of the paper, which detected three specific magnetic pointing superconductivity in the samples. He also described improved synthesis methods.

The LK99 researcher from Hubei, China, who said that the paper might not be released before the Lunar New Year because of patent issues, announced the main findings of the paper, which detected three specific magnetic pointing superconductivity in the samples. pic.twitter.com/7ytSWO0zN2

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We present the gravitational-wave background and its properties focusing on the background from compact binary coalescences in terrestrial detectors. We also introduce the standard data analysis method used to search for this background and discuss its detectability with second and third generation networks of detectors. To illustrate, we first use simple models and then discuss more realistic models based on simulations.

Discover how a simple blood test is transforming diabetes prediction, outperforming complex methods, all thanks to the power of mathematical modeling.

Join the discussion on this paper page.

We’re one step further down the path to limitless clean power.

Almost 1 in 5 Indian adults aged 60+ show signs of mild neurocognitive disorder, according to a study.

Given an estimated 138 million adults over 60 years of age in India, these estimates suggest approximately 24 million and 9.9 million older adults in India are living with mild and major neurocognitive disorder, respectively, revealed the study published in the journal PLOS One.

An advanced human heart organoid system can be used to model embryonic heart development under pregestational diabetes-like conditions, researchers report in the journal Stem Cell Reports.

The organoids recapitulate hallmarks of pregestational diabetes-induced congenital heart disease found in mice and humans. The findings also showed that endoplasmic reticulum (ER) stress and lipid imbalance are critical factors contributing to these disorders, which could be ameliorated with exposure to omega-3s.

Continue reading “Heart organoids simulate pregestational diabetes-induced congenital heart disease” »

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as in typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc.

However, previous designs required prior knowledge of materials or structures for different applications, and the designed WS-TEs usually vary from application to application in terms of materials and structures, thus there is no general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials.

In a new paper published in Light: Science & Applications, a team of scientists, led by Professor Run Hu from School of Energy and Power Engineering, Huazhong University of Science and Technology, China, and coworkers have proposed a general deep learning framework based on the deep Q-learning network algorithm (DQN) for efficient optimal design of WS-TEs across different applications.