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Abstract algebra unlocks distinguishable states for quantum systems

Researchers around the world are racing to develop new quantum-based systems for sensing, communication, computing and control that have the promise of outperforming traditional systems. Creating stable, measurable, distinguishable quantum states—which would be the heart of any such system—is a daunting task.

Quantum states possess unique properties that can be exploited to develop novel information-processing systems. Two key properties, stability and distinguishability, are hard to achieve, however. Extracting information from a quantum system depends on the distinguishability of quantum states, an intrinsic property associated with a property known as orthogonality. Nevertheless, no two Gaussian states (a widely studied class of quantum states) are orthogonal, and this yields an unavoidable error when attempting to distinguish them.

In addition, present quantum devices tend to remain stable only for a fraction of a second and require complex protocols to distinguish states. Now, researchers at MIT and the University of Ferrara have found a new approach for creating easily distinguishable states that could help enable the development of these new quantum-based devices.

Brain-computer interface enables independent, accurate communication for man living with ALS

A new study demonstrates that a person with severe paralysis caused by amyotrophic lateral sclerosis (ALS) can use a brain-computer interface (BCI) at home to communicate, work and interact with the digital world—without the need for researcher support. Published in Nature Medicine, the results mark a significant step toward delivering practical assistive technology for people with severe speech and motor impairments.

The BCI system was developed at UC Davis, in collaboration with colleagues at Brown University and Mass General Brigham Neuroscience Institute. It is equipped with advanced decoding algorithms that translate neural signals into text (speech BCI) and enable cursor control (movement BCI). It allows full interaction with a personal computer.

The brain-computer interface is designed to restore communication and computer control by decoding neural activity linked to attempted speech and movement. Although recent advances have achieved high accuracy in research settings, real-world adoption has been limited by two key challenges: independent at-home use and reliable long-term performance.

Passive quantum error correction doubles qubit lifetime, reaching break-even point

A team of U.S. researchers has designed a passive quantum error correction technique that enables qubits to correct their own errors. Demonstrated by Shruti Shirol and colleagues at the University of Massachusetts Amherst, the protocol transforms the inevitable dissipation of energy in qubit systems from a hindrance into an advantage, offering a promising route toward practical quantum computing outside the lab. The research has been published in Physical Review X.

As the building blocks of quantum computers, qubits aren’t limited to being either a 0 or a 1, like the classical bits that computers use today. Instead, they can exist in quantum superpositions of these states, offering new ways of storing and processing information.

However, these states are notoriously fragile. As they interact with vibrations and impurities in their surroundings, they can easily be destroyed, resulting in energy being dissipated from the system. To date, this poses one of the biggest roadblocks to building quantum computers in realistic settings outside the lab.

Tiny chip could help cameras spot hidden details

A tiny new chip could give cameras and sensing systems a far sharper view of the world, helping them detect subtle differences in materials and environments that standard color imaging systems cannot see.

In research led by Zhejiang University in collaboration with RMIT University, scientists have demonstrated a new way to build light-analysis capability directly into imaging hardware.

Cameras are highly effective at capturing images, but applications such as machine vision, automated inspection and environmental monitoring depend on understanding different colors and wavelengths of light, not just what something looks like. That information can reveal differences in materials, surface conditions or environmental changes that appear identical to the human eye.

Alonzo Church

His revolutionary idea? Before “computer science” was even a field, Church invented the lambda calculus (λ-calculus)—an elegant, abstract system for expressing computation through pure mathematical functions. In 1936, he used it to prove that no universal algorithm could ever decide the truth of all mathematical statements, solving Hilbert’s famous Entscheidungsproblem in the negative. This became known as Church’s Theorem, and it revealed something profound: there are hard limits to what any machine can compute.

That same year, Church articulated what we now call the Church–Turing thesis: any problem that can be “effectively calculated” can be computed by a Turing machine—or equivalently, expressed in lambda calculus. When Alan Turing learned of Church’s work, he traveled to Princeton to study under him. Together, they proved their two seemingly different models of computation were fundamentally equivalent, laying the bedrock for all future computer science.


Alonzo Church was born on June 14, 1903, in Washington, D.C., where his father, Samuel Robbins Church, was a justice of the peace [ 5 ] and the judge of the Municipal Court for the District of Columbia. He was the grandson of Alonzo Webster Church (1829−1909), United States Senate Librarian from 1881 to 1901, and great-grandson of Alonzo Church, a professor of Mathematics and Astronomy and 6th President of the University of Georgia. [ 6 ] As a young boy, Church was partially blinded by an air gun accident. [ 7 ] The family later moved to Virginia after his father lost his position at the university because of failing eyesight. With help from his uncle, also named Alonzo Church, the son attended the private Ridgefield School for Boys in Ridgefield, Connecticut. [ 8 ] After graduating from Ridgefield in 1920, Church attended Princeton University, where he was an exceptional student. He published his first paper on Lorentz transformations [ 9 ] in 1924 and graduated the same year with a degree in mathematics. He stayed at Princeton for graduate work, earning a Ph. D. in mathematics in three years under Oswald Veblen.

He married Mary Julia Kuczinski in 1925. The couple had three children: Alonzo Jr. (1929), Mary Ann (1933), and Mildred (1938).

After receiving his Ph.D., he taught briefly as an instructor at the University of Chicago. [ 10 ] He received a two-year National Research Fellowship that enabled him to attend Harvard University in 1927–1928, and the University of Göttingen and University of Amsterdam the following year.

Why Technological Civilizations Might Be Insanely Rare

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REFERENCES

Frontiers: For nearly a decade, the idea that ‘the body keeps the score’ has shaped public and clinical understanding of trauma (van der Kolk, 2014)

It is an enticing metaphor—implying that experience is literally inscribed in flesh, that the body bears the scars of what the mind cannot face. Yet recent advances in computational and systems neuroscience reveal that this image, while emotionally compelling, is biologically inaccurate. The body proper does not store trauma; the brain dynamically reenacts it through maladaptive inference. What endures after trauma is not a memory lodged in non-innervated tissue but a collapse of flexibility—a loss of metastability, the brain’s ability to fluidly switch among semi-stable network states.

In computational terms, trauma over-weights the precision of danger priors: the brain assigns excessive confidence to threat predictions, constraining inference based on the prior premise of enduring danger. The result is hypervigilance, flashbacks, and avoidance—symptoms of a system caught in self-confirming predictions. Mathematically, this overconfidence means one cannot escape local minima—in a free energy landscape—that become deeply and precisely engrained (i.e., trapped in a ravine with steep sides, where precision corresponds to the local curvature or steepness).

This rigidity contrasts with a healthy brain’s metastable dynamics, where neuronal networks continually integrate and segregate in response to context. This allows neuronal dynamics to explore multiple (unstable) interpretations of the world. Hellyer and colleagues demonstrated that metastability is a hallmark of cognitive flexibility: the capacity for neural coalitions to assemble transiently and adapt quickly. Using both empirical and computational approaches, Hellyer et al. (2015) showed that reduced metastability arising from damage to the structural connectome was associated with diminished cognitive flexibility and impaired information processing. Trauma erodes this fluidity, trapping the brain in narrow basins of fear and defensive salience. To restore mental health is not about ‘releasing’ stored emotion but reestablishing dynamic equilibrium enabling the brain’s ability to move with graceful agility over a landscape of beliefs, commitments and intentions.

New Discoveries Challenge Everything We Knew About Brain Evolution

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Hello and welcome! My name is Anton and in this video, we will talk about a few studies that explain how the human brain developed complexity.
Links:
https://linkinghub.elsevier.com/retri
Other videos:
• Surprise Evidence That Gut Microbes Direct…
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#brain #biology #evolution.

0:00 Discoveries about the evolution of the brain.
1:20 800 Million years ago… how it all began.
3:10 Did nervous system evolve multiple times? Comb jellies.
4:45 Big brains — primates vs octopuses.
9:20 Human brains and human intelligence genes.
11:20 Gut microbes and fuel for the brain.
12:20 Conclusions and implications.

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