Quantum Darwinism: Zurek argue that certain quantum states ("pointer states") are better at creating multiple, identical copies of themselves in the environment through entanglement. This "survival of the fittest" information is what we perceive as classical reality. The environment essentially "selects" these states, leading to a shared, objective reality.
A new study published in Physical Review Letters demonstrates that robust information storage is more complex than previously understood. Researchers used machine learning to discover multiple new classes of two-dimensional memories capable of reliably storing information despite constant environmental noise, moving beyond the traditionally known Toom's rule. The research reveals that noise can sometimes *stabilize* memories, and that standard theoretical models often fail to predict the behavior of these systems, highlighting the importance of fluctuations. This work has implications for quantum error correction and understanding how robust behavior emerges in complex systems.
A physicist explores the simulation hypothesis – the idea that our reality could be a computer simulation – and its implications, drawing on philosophy, technology, and scientific observations.
Researchers have found that even seemingly random events, like the roll of a die, are governed by fundamental laws of physics. Their work provides further evidence for a long-held belief that the universe is fundamentally deterministic, even if it appears chaotic.
In essence, the study reinforces that the Boltzmann distribution isn't just *a* way to model randomness, it's *the* way to model truly independent random systems.
In essence, the study reinforces that the Boltzmann distribution isn't just *a* way to model randomness, it's *the* way to model truly independent random systems.
Mathematicians are making progress on a decades-old problem about the Fourier transform by using techniques from graph theory, revealing unexpected connections between these fields.
Researchers have crafted a detailed string theory model compatible with the universe’s accelerated expansion, offering a potential solution to a long-standing problem in theoretical physics.
This article presents a compelling argument that the Manifold-Constrained Hyper-Connections (mHC) method in deep learning isn't just a mathematical trick, but a fundamentally physics-inspired approach rooted in the principle of energy conservation.
The author argues that standard neural networks act as "active amplifiers," injecting energy and potentially leading to instability. mHC, conversely, aims to create "passive systems" that route information without creating or destroying it. This is achieved by enforcing constraints on the weight matrices, specifically requiring them to be doubly stochastic.
The derivation of these constraints is presented from a "first principles" physics perspective:
* **Conservation of Signal Mass:** Ensures the total input signal equals the total output signal (Column Sums = 1).
* **Bounding Signal Energy:** Prevents energy from exploding by ensuring the output is a convex combination of inputs (non-negative weights).
* **Time Symmetry:** Guarantees energy conservation during backpropagation (Row Sums = 1).
The article also draws a parallel to Information Theory, framing mHC as a way to combat the Data Processing Inequality by preserving information through "soft routing" – akin to a permutation – rather than lossy compression.
Finally, it explains how the Sinkhorn-Knopp algorithm is used to enforce these constraints, effectively projecting the network's weights onto the Birkhoff Polytope, ensuring stability and adherence to the laws of thermodynamics. The core idea is that a stable deep network should behave like a system of pipes and valves, routing information without amplifying it.
The author argues that standard neural networks act as "active amplifiers," injecting energy and potentially leading to instability. mHC, conversely, aims to create "passive systems" that route information without creating or destroying it. This is achieved by enforcing constraints on the weight matrices, specifically requiring them to be doubly stochastic.
The derivation of these constraints is presented from a "first principles" physics perspective:
* **Conservation of Signal Mass:** Ensures the total input signal equals the total output signal (Column Sums = 1).
* **Bounding Signal Energy:** Prevents energy from exploding by ensuring the output is a convex combination of inputs (non-negative weights).
* **Time Symmetry:** Guarantees energy conservation during backpropagation (Row Sums = 1).
The article also draws a parallel to Information Theory, framing mHC as a way to combat the Data Processing Inequality by preserving information through "soft routing" – akin to a permutation – rather than lossy compression.
Finally, it explains how the Sinkhorn-Knopp algorithm is used to enforce these constraints, effectively projecting the network's weights onto the Birkhoff Polytope, ensuring stability and adherence to the laws of thermodynamics. The core idea is that a stable deep network should behave like a system of pipes and valves, routing information without amplifying it.
New research suggests that the very structure of space-time may be an emergent phenomenon, arising from the entanglement of quantum information. This challenges our fundamental understanding of gravity and the universe.
Mathematicians are using Srinivasa Ramanujan's century-old formulae to push the boundaries of high-performance computing and verify the accuracy of calculations.
Analysis of 15 years of Fermi LAT data reveals a statistically significant halo-like excess in gamma rays around 20 GeV, potentially originating from dark matter annihilation. The study examines systematic uncertainties and explores implications for WIMP parameters.
