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Scientists capture quantum uncertainty in real time
www.perplexity.ai/page/scientists-capture-quantum-unc-NsehXYrXTkekE91MHVol6wResearchers have captured and controlled quantum uncertainty in real time for the first time, fundamentally redefining the Heisenberg uncertainty principle as a dynamic, tunable property rather than a fixed limitation. This breakthrough, achieved with attosecond (10⁻¹⁸ seconds) precision, enables unprecedented observation and manipulation of quantum states as they evolve naturally, marking a major advance in ultrafast quantum optics.
Central to the achievement is the generation of ultrafast squeezed light pulses—quantum states where uncertainty is redistributed rather than eliminated. Unlike ordinary light, whose uncertainty is spread evenly between paired properties like phase and amplitude, squeezed light narrows uncertainty in one property at the cost of increasing it in the other. Researchers produced the shortest, most precisely controlled attosecond squeezed pulses to date by using nonlinear four-wave mixing in silicon dioxide combined with a custom light field synthesizer that combines multiple carefully phased spectral channels.
This experimental method allowed the team to dynamically switch between amplitude squeezing and phase squeezing in real time, showing that quantum uncertainty can be actively modulated rather than being a static bound. By splitting engineered waveforms into a classical reference and a squeezed beam and precisely measuring their intensity and phase fluctuations, the researchers quantified and controlled quantum noise below the standard quantum limit with attosecond temporal resolution.
The technological implications extend strongly into quantum communications, where the team demonstrated a petahertz-scale encryption protocol embedding information directly in the fluctuating quantum uncertainty patterns. This introduces a robust intrinsic security layer: eavesdropping disturbs the quantum state and also requires knowledge of a decoding key and exact pulse amplitude, making unauthorized interception detectable and decoding practically impossible. This promises ultrafast, highly secure data transfer networks leveraging quantum properties.
Further applications include enhanced quantum sensing, where tailored uncertainty control can improve measurement sensitivity beyond classical limits, enabling breakthroughs in navigation, environmental sensing, and medical diagnostics. The ability to manipulate quantum noise dynamically also points toward future quantum computing architectures operating at extreme speeds and precision, potentially at attosecond timescales.
This discovery, achieved through the intersection of nonlinear optics, laser physics, and quantum theory, transforms quantum uncertainty from a passive obstacle to an actively exploitable resource. It opens new frontiers in fundamental quantum physics and sets the stage for revolutionary quantum technologies far beyond existing capabilities. By bridging attosecond temporal control with quantum state engineering, the work marks a pivotal step toward harnessing the full potential of quantum mechanics in real-world applications and advanced scientific research.
AI breakthrough enables solving Einstein's field equations
www.perplexity.ai/page/ai-breakthrough-enables-solvin-l2E6.UfvSP2IECJcBxEsQgThis new approach to physics-informed neural networks (PINNs) enables robust solutions for stiff and high-dimensional differential equations by combining multi-head architectures and unimodular regularization. The multi-head strategy allows a single neural net to solve an entire family of equations simultaneously, improving generalization and handling noisy or sparse data. Unimodular regularization, leveraging ideas from differential geometry, stabilizes training and allows the system to efficiently uncover unknown or missing physical laws.
Applications are wide-ranging:
Astrophysics and Relativity: Direct solution of Einstein Field Equations and the modeling of complex spacetime geometries.
Climate Science: Modeling atmospheric dynamics and coupled climate models that involve many scales and stiff systems.
Chemical Kinetics and Biology: Simulation and inference in biochemical networks, metabolic pathways, and reaction-diffusion systems with rapid and slow processes intertwined.
Engineering: Fluid dynamics (including turbulent and reactive flows), aerodynamics, material deformation, and control systems where traditional solvers fail due to stiffness or sensitivity.
Environmental Science: Predictive modeling for air pollution, PM2.5 evolution, and other multi-timescale diffusion-advection problems.
The net result: faster, more accurate, and more versatile model training and simulation for any field that relies on solving or inferring complex differential equations under challenging data or physical constraints.
Refs:
[1] EPINN: Physics-Informed Neural Network with exponential activation functions for solving stiff ODEs
[2] Solving stiff ordinary differential equations using physics informed neural networks (PINNs): simple recipes to improve training of vanilla-PINNs
[3] Stiff neural ordinary differential equations
[4] Stabilize physics-informed neural networks for stiff differential equations: Re-spacing layer
[5] Mixing Differential Equations and Neural Networks for Physics-Informed Learning
[6] Training stiff neural ordinary differential equations with implicit single-step methods