Lifeboat Foundation

Near-term quantum computers, quantum computers developed today or in the near future, could help to tackle some problems more effectively than classical computers. One potential application for these computers could be in physics, chemistry and materials science, to perform quantum simulations and determine the ground states of quantum systems.

Some quantum computers developed over the past few years have proved to be fairly effective at running quantum simulations. However, near-term quantum computing approaches are still limited by existing hardware components and by the adverse effects of background noise.

Researchers at 1QB Information Technologies (1QBit), University of Waterloo and the Perimeter Institute for Theoretical Physics have recently developed neural error mitigation, a new strategy that could improve ground state estimates attained using quantum simulations. This strategy, introduced in a paper published in Nature Machine Intelligence, is based on machine-learning algorithms.

Van der Waerden’s conjecture mystified mathematicians for 85 years. Its solution shows how polynomial roots relate to one another.

Google AI announced the launch of the Google Research YouTube channel today. The channel is set to focus on a wide range of subjects like AI/ML, robotics, theory and algorithms, quantum computing, health and bioscience.

A new generation of AI image tools can reproduce an artist’s signature style. Some creatives fear for their livelihoods.

Quantum Information Science / Quantum Computing (QIS / QC) continues to make substantial progress into 2023 with commercial applications coming where difficult practical problems can be solved significantly faster using QC (quantum advantage) and QC solving seemingly impossible problems and test cases (not practical problems) that for classical computers such as supercomputers would take thousands of years or beyond classical computing capabilities (quantum supremacy). Often the two terms are interchanged. Claims of quantum advantage or quantum supremacy, at times, are able to be challenged through new algorithms on classical computers.

The potential is for hybrid systems with quantum computers and classical computers such as supercomputers (and perhaps analog computing in the future) could operate in the thousands and potentially millions of times faster in lending more understanding into intractable challenges and problems. Imagine the possibilities and the implications for the benefit of Earth’s ecosystems and humankind significantly impacting in dozens of areas of computational science such as big data analytics, weather forecasting, aerospace and novel transportation engineering, novel new energy paradigms such as renewable energy, healthcare and drug discovery, omics (genomics, transcriptomics, proteomics, metabolomic), economics, AI, large-scale simulations, financial services, new materials, optimization challenges, … endless.

The stakes are so high in competitive and strategic advantage that top corporations and governments are investing in and working with QIS / QC. (See my Forbes article: Government Deep Tech 2022 Top Funding Focus Explainable AI, Photonics, Quantum—they (BDC Deep Tech Fund) invested in QC company Xanadu). For the US, in 2018, there is the USD $1.2 billion National Quantum Initiative Act and related U.S. Department of Energy providing USD $625 million over five years for five quantum information research hubs led by national laboratories: Argonne, Brookhaven, Fermi, Lawrence Berkeley and Oak Ridge. In August 2022, the US CHIPS and Science Act providing hundreds of millions in funding as well. Coverage includes: accelerating the discovery of quantum applications; growing a diverse and domestic quantum workforce; development of critical infrastructure and standardization of cutting-edge R&D.

Color coding makes aerial maps much more easily understood. Through color, we can tell at a glance where there is a road, forest, desert, city, river or lake.

Working with several universities, the U.S. Department of Energy’s (DOE) Argonne National Laboratory has devised a method for creating color-coded graphs of large volumes of data from X-ray analysis. This new tool uses computational data sorting to find clusters related to physical properties, such as an atomic distortion in a crystal structure. It should greatly accelerate future research on structural changes on the atomic scale induced by varying temperature.

The research team published their findings in the Proceedings of the National Academy of Sciences in an article titled “Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction.”

Engineers have created intelligent 3D printers that can quickly detect and correct errors, even in previously unseen designs, or unfamiliar materials like ketchup and mayonnaise, by learning from the experiences of other machines.

The engineers, from the University of Cambridge, developed a machine learning algorithm that can detect and correct a wide variety of different errors in real time, and can be easily added to new or existing machines to enhance their capabilities. 3D printers using the algorithm could also learn how to print new materials by themselves. Details of their low-cost approach are reported in the journal Nature Communications.

3D printing has the potential to revolutionize the production of complex and customized parts, such as aircraft components, personalized medical implants, or even intricate sweets, and could also transform manufacturing supply chains. However, it is also vulnerable to production errors, from small-scale inaccuracies and mechanical weaknesses through to total build failures.

An artificial intelligence (AI) algorithm to detect subtle brain abnormalities that cause epileptic seizures has been developed. The abnormalities, known as focal cortical dysplasias (FCDs), can often be treated with surgery but are difficult to visualize on an MRI. The new algorithm is expected to give physicians greater confidence in identifying FCDs in patients with epilepsy.

The work, which was part of the Multicentre Epilepsy Lesion Detection (MELD) project, appeared in Brain “Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.” Konrad Wagstyl, PhD, and Sophie Adler, PhD, both from University College London, led an international team of researchers on the work.

To develop the algorithm, the team quantified features of the brain cortex—such as thickness and folding—in more than 1,000 patient MRI scans from 22 epilepsy centers around the world. They then trained the algorithm on examples labeled by expert radiologists as either being healthy or having FCD.

Ever since Archimedes, mathematicians have been fascinated by equations that involve a difference between squares. Now two mathematicians have proven how often these equations have solutions, concluding a decades-old quest.