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Categories: Chemistry: Organic Chemistry, Computer Science: General

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Quantum computers promise to reach speeds and efficiencies impossible for even the fastest supercomputers of today. Yet the technology hasn't seen much scale-up and commercialization largely due to its inability to self-correct. Quantum computers, unlike classical ones, cannot correct errors by copying encoded data over and over. Scientists had to find another way. Now, a new paper illustrates a quantum computing platform's potential to solve the longstanding problem known as quantum error correction.

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Scientists have developed a technique called 'Time-dependent Stochastic Parameter Shift' in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.

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Researchers have developed an easy-to-use optical chip that can configure itself to achieve various functions. The positive real-valued matrix computation they have achieved gives the chip the potential to be used in applications requiring optical neural networks.

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Researchers have demonstrated a type of quantum eraser. The physicists show that they can pinpoint and correct for mistakes in quantum computing systems known as 'erasure' errors. 

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Artificial intelligence (AI) comes with promises of helping coders code faster, drivers drive safer, and making daily tasks less time-consuming. But a recent study demonstrates that the tool, when adopted widely, could have a large energy footprint, which in the future may exceed the power demands of some countries.

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A team of computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks--the engines that drive artificial intelligence and machine learning--thereby illuminating a process that has largely been concealed from users.   

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A fundamental discovery about the Fischer Tropsch process, a catalytic reaction used in industry to convert coal, natural gas or biomass to liquid fuels, could someday allow for more efficient fuel production. Researchers discovered previously unknown self-sustained oscillations in the Fischer Tropsch process. They found that unlike many catalytic reactions which have one steady state, this reaction periodically moves back and forth from a high to a low activity state. The discovery means that these well-controlled oscillatory states might be used in the future to control the reaction rate and the yields of desired products.

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Imagine a life form that doesn't resemble any of the organisms found on the tree of life. One that has its own unique control system, and that a doctor would want to send into your body. It sounds like a science fiction movie, but according to nanoscientists, it can—and should—happen in the future.

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Whether you’re battling foes in a virtual arena or collaborating with colleagues across the globe, lag-induced disruptions can be a major hindrance to seamless communication and immersive experiences. That’s why researchers have developed new technology to make data transfer over optical fiber communication faster and more efficient.

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Big data and artificial intelligence (AI) are being used to model hidden patterns in nature, not just for one bird species, but for entire ecological communities across continents. And the models follow each species’ full annual life cycle, from breeding to fall migration to non-breeding grounds, and back north again during spring migration.

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For reasons of food security and economic incentive, farmers continuously seek to maximize their marketable crop yields. As plants grow inconsistently, at the time of harvesting, there will inevitably be variations in quality and size of individual crops. Finding the optimal time to harvest is therefore a priority for farmers. A new approach making heavy use of drones and artificial intelligence demonstrably improves this estimation by carefully and accurately analyzing individual crops to assess their likely growth characteristics.

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A probable early driver of Alzheimer's disease is the accumulation of molecules called amyloid peptides. These cause cell death, and are commonly found in the brains of Alzheimer’s patients. Researchers have now shown that yeast cells that accumulate these misfolded amyloid peptides can recover after being treated with graphene oxide nanoflakes.

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Researchers developed the first AI to date that can intelligently design robots from scratch by compressing billions of years of evolution into mere seconds. It's not only fast but also runs on a lightweight computer and designs wholly novel structures from scratch — without human-labeled, bias-filled datasets.

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Researchers have pushed forward the boundaries of biomedical engineering one hundredfold with a new method for DNA detection with unprecedented sensitivity.

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At the core of uncovering extreme events such as floods is the physics of fluids – specifically turbulent flows. Researchers leveraged a computer-vision deep learning technique and adapted it for nonlinear analysis of extreme events in wall-bounded turbulent flows, which are pervasive in numerous physics and engineering applications and impact wind and hydrokinetic energy, among others. Results show the technique employed can be invaluable for accurately identifying the sources of extreme events in a completely data-driven manner.

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A new CRISPR-based gene-editing tool has been developed which could lead to better treatments for patients with genetic disorders. The tool is an enzyme, AsCas12f, which has been modified to offer the same effectiveness but at one-third the size of the Cas9 enzyme commonly used for gene editing. The compact size means that more of it can be packed into carrier viruses and delivered into living cells, making it more efficient.

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Scientists have used cutting-edge imaging techniques to shed light on the progression of Parkinson's disease by studying how the main culprit, the protein alpha-synuclein, disrupts cellular metabolism.

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A technology called cryo-electron microscopy enables scientists to see the atomic structure of biological molecules in high resolution. But to date, it has been ineffective for imaging small molecules. A team of biochemists devised a solution that makes it possible to hold small protein molecules in place while they're being imaged, which will enable cryo-EM to produce much clearer images of such molecules. The advance is significant because small and medium-sized protein molecules are an area of focus in research on potential new drugs for cancer and other diseases.

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Newly developed ompounds aim to reveal the dual nature of formaldehyde, a chemical that is known to cause cancer but is also believed to play important roles in our biology.

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Scientists mapped out the three-dimensional structure of one of the smallest known CRISPR-Cas13 systems then used that knowledge to modify its structure and improve its accuracy.