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DNA as a perfect quantum computer based on the quantum physics principles

I believe that dna will be able to answer just about all our genetic coding questions so much that it will lead to even better breakthroughs in the future and use hardly any energy. I believe also that the master algorithm can eventually be derived from DNA as dna seems already a perfect master algorithm for human beings where human beings are the key to all future progress. I say this as quantum computing is still not stable but we already know that dna computers seem already a masterpiece already especially even organoids of the human brain. Really it becomes really quite simple as even the quantum realm is unstable but dna computers that are quantum would stabilize this currently unstable realm.

Riera Aroche, R., Ortiz García, Y.M., Martínez Arellano, M.A. et al. DNA as a perfect quantum computer based on the quantum physics principles. Sci Rep 14, 11,636 (2024). https://doi.org/10.1038/s41598-024-62539-5

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Faster topology optimization: An emerging industrial design technique gets a speed boost

With the rise of 3D printing and other advanced manufacturing methods, engineers can now build structures that were once impossible to fabricate. An emerging design strategy that takes full advantage of these new capabilities is topology optimization—a computer-driven technique that determines the most effective way to distribute material, leading to an optimized design.

Now, a research team including mathematicians from Brown University has developed a new approach that dramatically improves the speed and stability of topology optimization algorithms. The team, a collaboration between researchers at Brown, Lawrence Livermore National Laboratory and Simula Research Laboratory in Norway, detailed their work in two recently published papers in the SIAM Journal on Optimization and Structural and Multidisciplinary Optimization.

“Our method beats some existing methods by four or five times in terms of efficiency,” said Brendan Keith, an assistant professor of applied mathematics at Brown. “That’s a huge computational savings that could enable people to make designs more quickly and inexpensively, or to develop more complex designs with higher resolution.”

Ultrafast 12-minute MRI maps brain chemistry to spot disease before symptoms

Illinois engineers fused ultrafast imaging with smart algorithms to peek at living brain chemistry, turning routine MRIs into metabolic microscopes. The system distinguishes healthy regions, grades tumors, and forecasts MS flare-ups long before structural MRI can. Precision-medicine neurology just moved closer to reality.

Navier–Stokes existence and smoothness

The problem concerns the mathematical properties of solutions to the Navier–Stokes equations, a system of partial differential equations that describe the motion of a fluid in space. Solutions to the Navier–Stokes equations are used in many practical applications. However, theoretical understanding of the solutions to these equations is incomplete. In particular, solutions of the Navier–Stokes equations often include turbulence, which remains one of the greatest unsolved problems in physics, despite its immense importance in science and engineering.

New hybrid quantum–classical computing approach used to study chemical systems

Caltech professor of chemistry Sandeep Sharma and colleagues from IBM and the RIKEN Center for Computational Science in Japan are giving us a glimpse of the future of computing. The team has used quantum computing in combination with classical distributed computing to attack a notably challenging problem in quantum chemistry: determining the electronic energy levels of a relatively complex molecule.

The work demonstrates the promise of such a quantum–classical hybrid approach for advancing not only , but also fields such as , nanotechnology, and drug discovery, where insight into the electronic fingerprint of materials can reveal how they will behave.

“We have shown that you can take classical algorithms that run on high-performance classical computers and combine them with quantum algorithms that run on quantum computers to get useful chemical results,” says Sharma, a new member of the Caltech faculty whose work focuses on developing algorithms to study quantum . “We call this quantum-centric supercomputing.”

Reports in Advances of Physical Sciences

In this paper, the authors propose a three-dimensional time model, arguing that nature itself hints at the need for three temporal dimensions. Why three? Because at three different scales—the quantum world of tiny particles, the realm of everyday physical interactions, and the grand sweep of cosmological evolution—we see patterns that suggest distinct kinds of “temporal flow.” These time layers correspond, intriguingly, to the three generations of fundamental particles in the Standard Model: electrons and their heavier cousins, muons and taus. The model doesn’t just assume these generations—it explains why there are exactly three and even predicts their mass differences using mathematics derived from a “temporal metric.”

This paper introduces a theoretical framework based on three-dimensional time, where the three temporal dimensions emerge from fundamental symmetry requirements. The necessity for exactly three temporal dimensions arises from observed quantum-classical-cosmological transitions that manifest at three distinct scales: Planck-scale quantum phenomena, interaction-scale processes, and cosmological evolution. These temporal scales directly generate three particle generations through eigenvalue equations of the temporal metric, naturally explaining both the number of generations and their mass hierarchy. The framework introduces a metric structure with three temporal and three spatial dimensions, preserving causality and unitarity while extending standard quantum mechanics and field theory.

Tesla Robotaxi Vs. Waymo

Tesla is planning to launch a robo-taxi service in Austin, Texas, which is expected to disrupt the market with its competitive advantages in data collection, cost, and production, shifting the company’s business model towards recurring software revenue ## ## Questions to inspire discussion.

Tesla’s Robotaxi Launch.

🚗 Q: When is Tesla launching its robotaxi service? A: Tesla’s robotaxi launch is scheduled for June 22nd, marking a transformational shift from hardware sales to recurring software revenue with higher margins.

🌆 Q: How will Tesla’s robotaxi service initially roll out? A: The service will start with a small fleet of 10–20 vehicles, scaling up to multiple cities by year-end and millions of cars by next year’s end, with an invite-only system initially. Tesla vs. Waymo.

📊 Q: How does Tesla’s data collection compare to Waymo’s? A: Tesla collects 10 million miles of full self-driving data daily, compared to Waymo’s 250,000 miles, giving Tesla a significant data advantage for training AI and encountering corner cases.

🏭 Q: What production advantage does Tesla have over Waymo? A: Tesla can produce 5,000 vehicles per day, while Waymo has 1,500 vehicles with plans to add 200,000 over the next year, giving Tesla a substantial cost and scale advantage.

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All-topographic neural networks more closely mimic the human visual system

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a result, in addition to tackling various real-world computational problems, they could help neuroscientists and psychologists to better understand the underpinnings of specific sensory or cognitive processes.

Researchers at Osnabrück University, Freie Universität Berlin and other institutes recently developed a new class of artificial neural networks (ANNs) that could mimic the human visual system better than CNNs and other existing deep learning algorithms. Their newly proposed, visual system-inspired computational techniques, dubbed all-topographic neural networks (All-TNNs), are introduced in a paper published in Nature Human Behaviour.

“Previously, the most powerful models for understanding how the brain processes visual information were derived off of AI vision models,” Dr. Tim Kietzmann, senior author of the paper, told Tech Xplore.

Advanced algorithm to study catalysts on material surfaces could lead to better batteries

A new algorithm opens the door for using artificial intelligence and machine learning to study the interactions that happen on the surface of materials.

Scientists and engineers study the that happen on the surface of materials to develop more energy efficient batteries, capacitors, and other devices. But accurately simulating these fundamental interactions requires immense computing power to fully capture the geometrical and chemical intricacies involved, and current methods are just scratching the surface.

“Currently it’s prohibitive and there’s no supercomputer in the world that can do an analysis like that,” says Siddharth Deshpande, an assistant professor in the University of Rochester’s Department of Chemical Engineering. “We need clever ways to manage that large data set, use intuition to understand the most important interactions on the surface, and apply data-driven methods to reduce the sample space.”

50 Years Later, a Quantum Mystery Has Finally Been Solved

The quantum physics community is buzzing with excitement after researchers at Rice University finally observed a phenomenon that had eluded scientists for over 70 years. This breakthrough, recently published in Science Advances is known as the superradiant phase transition (SRPT), represents a significant milestone in quantum mechanics and opens extraordinary possibilities for future technological applications.

In 1954, physicist Robert H. Dicke proposed an intriguing theory suggesting that under specific conditions, large groups of excited atoms could emit light in perfect synchronization rather than independently. This collective behavior, termed superradiance, was predicted to potentially create an entirely new phase of matter through a complete phase transition.

For over seven decades, this theoretical concept remained largely confined to equations and speculation. The primary obstacle was the infamous “no-go theorem,” which seemingly prohibited such transitions in conventional light-based systems. This theoretical barrier frustrated generations of quantum physicists attempting to observe this elusive phenomenon.