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Diamond quantum sensors improve spatial resolution of MRI

This accomplishment breaks the previous record of 48 qubits set by Jülich scientists in 2019 on Japan’s K computer. The new result highlights the extraordinary capabilities of JUPITER and provides a powerful testbed for exploring and validating quantum algorithms.

Simulating quantum computers is essential for advancing future quantum technologies. These simulations let researchers check experimental findings and experiment with new algorithmic approaches long before quantum hardware becomes advanced enough to run them directly. Key examples include the Variational Quantum Eigensolver (VQE), which can analyze molecules and materials, and the Quantum Approximate Optimization Algorithm (QAOA), used to improve decision-making in fields such as logistics, finance, and artificial intelligence.

Recreating a quantum computer on conventional systems is extremely demanding. As the number of qubits grows, the number of possible quantum states rises at an exponential rate. Each added qubit doubles the amount of computing power and memory required.

Although a typical laptop can still simulate around 30 qubits, reaching 50 qubits requires about 2 petabytes of memory, which is roughly two million gigabytes. ‘Only the world’s largest supercomputers currently offer that much,’ says Prof. Kristel Michielsen, Director at the Jülich Supercomputing Centre. ‘This use case illustrates how closely progress in high-performance computing and quantum research are intertwined today.’

The simulation replicates the intricate quantum physics of a real processor in full detail. Every operation – such as applying a quantum gate – affects more than 2 quadrillion complex numerical values, a ‘2’ with 15 zeros. These values must be synchronized across thousands of computing nodes in order to precisely replicate the functioning of a real quantum processor.

The JUPITER supercomputer set a new milestone by simulating 50 qubits. New memory and compression innovations made this breakthrough possible. A team from the Jülich Supercomputing Centre, working with NVIDIA specialists, has achieved a major milestone in quantum research. For the first time, they successfully simulated a universal quantum computer with 50 qubits, using JUPITER, Europe’s first exascale supercomputer, which began operation at Forschungszentrum Jülich in September.

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Rules that Reality Plays By — Dr. Stephen Wolfram, DemystifySci #343

Stephen Wolfram is a physicist, mathematician, and programmer who believes he has discovered the computational rules that organize the universe at the finest grain. These rules are not physical rules like the equations of state or Maxwell’s equations. According to Wolfram, these are rules that govern how the universe evolves and operates at a level at least one step down below the reality that we inhabit. His computational principles are inspired by the results observed in cellular automata systems, which show that it’s possible to take a very simple system, with very simple rules, and end up at complex patterns that often look organic and always look far more intricate than the black and white squares that the game started with. He believes that the hyperspace relationships that emerge when he applies a computational rule over and over again represent the nature of the universe — and that the relationships that emerge contain everything from the seed of human experience to the equations for relativity, evolution, and black holes. We sit down with him for a conversation about the platonic endeavor that he has undertaken, where to draw the line between lived experience and the computational universe, the limits of physics, and the value of purpose and the source of consciousness.

MAKE HISTORY WITH US THIS SUMMER:
https://demystifysci.com/demysticon-2025

PATREON
/ demystifysci.

PARADIGM DRIFT
https://demystifysci.com/paradigm-drift-show.

Material solutions to quantum spookiness: https://www.youtube.com/@MaterialAtomics.

00:00 Go!

Continue reading “Rules that Reality Plays By — Dr. Stephen Wolfram, DemystifySci #343” | >

A new artificial muscle could let humanoid robots lift 4,400 times their weight

A new material bends that rule.

Researchers in South Korea say they have built a soft, magnetic artificial muscle that hits hard numbers without turning into a stiff piston. The material flexes, contracts and relaxes like flesh, yet ramps up stiffness on demand when asked to do real work. That mix has long sat out of reach for humanoid robots that need both agility and strength.

Most humanoids move with a cocktail of motors, gears and pneumatic lines. These systems deliver power, but they also add bulk and make contact risky. Soft actuators change the equation. They integrate into limbs, cushion impacts and tolerate misalignment. They also weigh far less than hydraulic stacks and slot neatly inside compact forms like hands, faces and torsos.

Biochemistry: Glucose Metabolism Overview Part II

GLUCOSE OXIDATION EQUATION Glucose + 6 O2 — 6 CO2 + 6 H2O + Energy (ATP + heat) • Most energy is generated in mitochondrial matrixCommon Abbreviations: • ATP: adenosine triphosphate • NADH: nicotinamide adenine dinucleotide • FADH2: flavin adenine dinucleotide • CoA: Coenzyme AKEY PROCESSES IN GLUCOSE OXIDATION • Glycolysis • Pyruvate decarboxylation • Citric acid cycle (also known as the Krebs’ cycle and the tri-carboxylic acid (TCA) cycle) • Oxidative phosphorylation (electron transport chain & chemiosmosis)CITRIC ACID CYCLE • 1 glucose molecule requires 2 citric acid cycle turns • Input for each turn: 1 Acetyl CoA • Output for each turn: 3 NADH + 2 CO2 + 1 ATP + 1 FADH2 • NADH & FADH2: electron transfer molecules for oxidative phosphorylation • Occurs in mitochondrial matrixSubstrate level phosphorylation • ATP generated from substrates in glycolysis and citric acid cycle • NOT from.

We’re (Probably) Not Alone Out Here… — YouTube

Give the most meaningful Christmas gift ✨ Create a custom star map from Under Lucky Stars at http://UnderLuckyStars.com.

Why haven’t we heard from aliens? That’s a question that sounds simple but turns into a mess the moment you try to answer it. Recently, a mathematician tried to simplify the equation by trying to calculate the odds that we’re the only intelligent life in the universe – according to his math, we shouldn’t be. Let’s take a look.

Paper: https://www.sciencedirect.com/science… Check out my new quiz app ➜ http://quizwithit.com/ 📚 Buy my book ➜ https://amzn.to/3HSAWJW 💌 Support me on Donorbox ➜ https://donorbox.org/swtg 📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/ 👉 Transcript with links to references on Patreon ➜ / sabine 📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle… 👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl… 🔗 Join this channel to get access to perks ➜ / @sabinehossenfelder #science #sciencenews #aliens #maths.

🤓 Check out my new quiz app ➜ http://quizwithit.com/
📚 Buy my book ➜ https://amzn.to/3HSAWJW
💌 Support me on Donorbox ➜ https://donorbox.org/swtg.
📝 Transcripts and written news on Substack ➜ https://sciencewtg.substack.com/
👉 Transcript with links to references on Patreon ➜ / sabine.
📩 Free weekly science newsletter ➜ https://sabinehossenfelder.com/newsle…
👂 Audio only podcast ➜ https://open.spotify.com/show/0MkNfXl…
🔗 Join this channel to get access to perks ➜
/ @sabinehossenfelder.

#science #sciencenews #aliens #maths

Potentially distinct structure in Kuiper belt discovered with help of clustering algorithm

A vast region of our solar system, called the Kuiper belt, stretches from the orbit of Neptune out to 50 or so astronomical units (AU), where an AU is the distance between Earth and the sun. This region consists mostly of icy objects and small rocky bodies, like Pluto. Scientists believe Kuiper belt objects (KPOs) are remnants left over from the formation of the solar system.

Now, a new preprint paper on arXiv describes a newly identified region that appears to be completely distinct from other parts of the Kuiper belt—but some uncertainty remains.

Machine learning algorithm rapidly reconstructs 3D images from X-ray data

Soon, researchers may be able to create movies of their favorite protein or virus better and faster than ever before. Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have pioneered a new machine learning method—called X-RAI (X-Ray single particle imaging with Amortized Inference)—that can “look” at millions of X-ray laser-generated images and create a three-dimensional reconstruction of the target particle. The team recently reported their findings in Nature Communications.

X-RAI’s ability to sort through a massive number of images and learn as it goes could unlock limits in data-gathering, allowing researchers to see molecules up close—and perhaps even on the move. “There is really no limit” to the dataset size it can handle, said SLAC staff scientist Frédéric Poitevin, one of the study’s principal investigators.

How small can optical computers get? Scaling laws reveal new strategies

The research, published in Nature Communications, addresses one of the key challenges to engineering computers that run on light instead of electricity: making those devices small enough to be practical. Just as algorithms on digital computers require time and memory to run, light-based systems also require resources to operate, including sufficient physical space for light waves to propagate, interact and perform analog computation.

Lead authors Francesco Monticone, associate professor of electrical and computer engineering, and Yandong Li, Ph.D. ‘23, postdoctoral researcher, revealed scaling laws for free-space optics and photonic circuits by analyzing how their size must grow as the tasks they perform become more complex.

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