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A broad systematic review has revealed that quantum computing applications in health care remain more theoretical than practical, despite growing excitement in the field.

The comprehensive study published in npj Digital Medicine, which analyzed 4,915 research papers published between 2015 and 2024, found little evidence that quantum machine learning (QML) algorithms currently offer any meaningful advantage over classical computing methods for health care applications.

“Despite in research claiming quantum benefits for health care, our analysis shows no consistent evidence that quantum algorithms outperform classical methods for clinical decision-making or health service delivery,” said Dr. Riddhi Gupta from the School of Mathematics and Physics and the Queensland Digital Health Center (QDHeC) at the University of Queensland.

A recent study has mathematically clarified how the presence of crystals and gas bubbles in magma affects the propagation of seismic P-waves. The researchers derived a new equation that characterizes the travel of these waves through magma, revealing how the relative proportions of crystals and bubbles influence wave velocity and waveform properties.

The ratio of crystals to bubbles in subterranean magma reservoirs is crucial for forecasting . Due to the inaccessibility of direct observations, scientists analyze seismic P-waves recorded at the surface to infer these internal characteristics.

Previous studies have predominantly focused on the influence of , with limited consideration given to crystal content. Moreover, conventional models have primarily addressed variations in wave velocity and amplitude decay, without capturing detailed waveform transformations.

Proteins are among the most studied molecules in biology, yet new research from the University of Göttingen shows they can still hold surprising secrets. Researchers have discovered previously undetected chemical bonds within archived protein structures, revealing an unexpected complexity in protein chemistry.

These newly identified nitrogen-oxygen-sulfur (NOS) linkages broaden our understanding of how proteins respond to , a condition where harmful oxygen-based molecules build up and can damage proteins, DNA, and other essential parts of the cell. The new findings are published in Communications Chemistry.

The research team systematically re-analyzed over 86,000 high-resolution protein structures from the Protein Data Bank, a global public repository of protein structures, using a new algorithm that they developed inhouse called SimplifiedBondfinder. This pipeline combines , quantum mechanical modeling, and structural refinement methods to reveal subtle that were missed by conventional analyses.

The qualia problem of perception is simply pointing out that the way we perceive the world is in terms of subjective qualities rather than numerical quantities. For example, we perceive the color of light in the things we see rather than the frequency of light wave vibrations or wavelengths, just as we perceive the quality of the sounds we hear rather than the frequency of sound wave vibrations. Another example is emotional qualities, like the perception of pleasure and pain and the perception of other emotional qualities, like the emotional qualities that color the perception of the emotional body feelings we perceive with emotional expressions of fear and desire. There is no possible way to understand the perception of these emotional qualities, just as there is no way to understand the perception of the colors we see or the qualities of the sounds we hear, in terms of the neuronal firing rates of neurons in the brain or other nervous systems. The frequency of wave vibrations and the neuronal firing rates of neurons are both examples of quantities. The problem is we do not perceive things in terms of numerical quantities, but rather in terms of subjective qualities.

All our physical theories are formulated in terms of numerical quantities, not in terms of subjective qualities. For example, in ordinary quantum theory or in quantum field theory, we speak of the frequency of light wave vibrations or the wavelength of a light wave in terms of a quantum particle called the photon. A photon or light wave is characterized by the numerical quantities of frequency and wavelength. When we formulate the nature of a light wave or photon in quantum theory in terms of Maxwell’s equations for the electromagnetic field, we can only describe numerical quantities. In ordinary quantum theory and quantum field theory, the electromagnetic field is the quantum wave-function, ψ(x, t), that specifies the quantum probability that the point particle called the photon can be measured at a position x in space at a moment t in time. That quantum probability is specified in terms of the frequency and wavelength that characterizes the wave-function for the photon.

Neural data analysis algorithms capable of tracking neuronal signals from one-photon functional imaging data longitudinally and reliably are still lacking. Here authors developed CaliAli, a tool for extracting calcium signals across multiple days. Validated with optogenetic tagging, dual-color imaging, and place cell data, CaliAli demonstrated stable neuron tracking for up to 99 days.

Physicists from Oxford have, for the first time, scaled quantum computing using distributed teleportation technology — and this could change everything. From «parallel universes» to Grover’s algorithm, from cryptography to molecular modeling — the world is entering an era where «impossible» problems

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers.

Guide for using DeepSeek on Lambda:
https://docs.lambdalabs.com/education/large-language-models/…dium=video.

📝 AlphaEvolve: https://deepmind.google/discover/blog/alphaevolve-a-gemini-p…lgorithms/
📝 My genetic algorithm for the Mona Lisa: https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/

📝 My paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD

Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5

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Deepnight’s Algorithm-intensified image enhancement for NIGHT VISION

Instead of using expensive image-intensification tubes, this startup is using ordinary low light sensors coupled with special computer algorithms to produce night vision. This will bring night vision to the general public. At present, even a generation 2 monocular costs around $2000, while a generation 3 device costs around $3500. The new system has the added advantage of being in color, instead of monochromatic. Hopefully, this will pan out, and change the situation for Astronomy enthusiasts worldwide.


Lucas Young, CEO of Deepnight, showcases how their AI technology transforms a standard camera into an affordable and effective night vision device in extremely dark environments.

Artificial intelligence is a broad term encompassing many different subtypes, from apps that can write poetry to algorithms that are able to spot patterns that would otherwise get missed – and now AI modeling has just played a major role in an Alzheimer’s study.