Physics News and Discussions

weatheriscool
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Quantum light source advances bio-imaging clarity
https://phys.org/news/2022-09-quantum-s ... arity.html
by Nancy Luedke, Texas A&M University College of Engineering

Texas A&M University researchers accomplished what was once considered impossible—they created a device capable of squeezing the quantum fluctuations of light down to a directed path and used it to enhance contrast imaging.

This one-of-a-kind "flashlight" was built to increase the signal-to-noise ratio present in Brillouin microscopy spectroscopic measurements that visually record the mechanical properties of structures inside living cells and tissues. Test results reveal the new source significantly increases image clarity and accuracy.

"This is a new avenue in research," said Dr. Vladislav Yakovlev, University Professor in the Department of Biomedical Engineering in the College of Engineering. "We are specially designing light in such a way that it can improve contrast."

"It's a new milestone in the capabilities of Brillouin microscopy and imaging extensively used for bio systems," said Dr. Girish Agarwal, University Distinguished Professor in the Department of Biological and Agricultural Engineering in the College of Agriculture and Life Sciences. "And it becomes part of an international effort to develop quantum sensors for diverse applications like brain imaging, biomolecule structure mapping and exploring underground oil and water sources by devising supersensitive gravimeters."
weatheriscool
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Physicists make molecular vibrations more detectable
https://phys.org/news/2022-09-physicist ... tions.html
by Eva Sittig, Christian-Albrechts-Universität zu Kiel

In molecules, the atoms vibrate with characteristic patterns and frequencies. Vibrations are therefore an important tool for studying molecules and molecular processes such as chemical reactions. Although scanning tunneling microscopes can be used to image individual molecules, their vibrations have so far been difficult to detect.

Physicists at Kiel University (Christian-Albrechts-Universität zu Kiel, CAU) have now invented a method with which the vibration signals can be amplified by up to a factor of 50. Furthermore, they increased the frequency resolution considerably. The new method will improve the understanding of interactions in molecular systems and further simulation methods. The research team has now published the results in the journal Physical Review Letters.

The discovery by Dr. Jan Homberg, Dr. Alexander Weismann and Prof. Dr. Richard Berndt from the Institute of Experimental and Applied Physics, relies on a special quantum mechanical effect, so-called "inelastic tunneling". Electrons that pass through a molecule on their way from a metal tip to the substrate surface in the scanning tunneling microscope can release energy to the molecule or take energy up from it. This energy exchange occurs in portions determined by the properties of the respective molecule.
weatheriscool
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Differentiating right- and left-handed particles using the force exerted by light
https://phys.org/news/2022-09-different ... erted.html
by National Institutes of Natural Sciences
Researchers investigated the polarization-dependence of the force exerted by circularly polarized light (CPL) by performing optical trapping of chiral nanoparticles. They found that left- and right-handed CPL exerted different strengths of the optical gradient force on the nanoparticles, and the D- and L-form particles are subject to different gradient force by CPL. The present results suggest that separation of materials according to their handedness of chirality can be realized by the optical force.

Chirality is the property that the structure is not superimposable on its mirrored image. Chiral materials exhibit the characteristic feature that they respond differently to left- and right-circularly polarized light. When matter is irradiated with strong laser light, optical force is exerted on it. It has been expected theoretically that the optical force exerted on chiral materials by left- and right-circularly polarized light would also be different.

The research group at Institute for Molecular Science and three other universities used an experimental technique of optical trapping to observe the circular-polarization dependent optical gradient force exerted on chiral gold nanoparticles. Chiral gold nanoparticles have either D-form (right-handed) or L-form (left-handed) structure, and the experiment was performed using both.
weatheriscool
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Researchers answer fundamental question of quantum physics
https://phys.org/news/2022-09-fundament ... ysics.html
by Michael Hallermayer, Universität Augsburg

An international team of physicists, with the participation of the University of Augsburg, has for the first time confirmed an important theoretical prediction in quantum physics. The calculations for this are so complex that they have hitherto proved too demanding even for supercomputers. However, the researchers succeeded in simplifying them considerably using methods from the field of machine learning. The study improves the understanding of fundamental principles of the quantum world. It has been published in the journal Science Advances.

The calculation of the motion of a single billiard ball is relatively simple. However, predicting the trajectories of a multitude of gas particles in a vessel which are constantly colliding, being slowed down and deflected, is way more difficult. But what if it is not even at all clear exactly how fast each particle is moving, so that they would have countless possible velocities at any given time, differing only in their probability?

The situation is similar in the quantum world: Quantum mechanical particles can even have all potentially possible properties simultaneously. This makes the state space of quantum mechanical systems extremely large. If you aim to simulate how quantum particles interact with each other, you have to consider their complete state spaces.
weatheriscool
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Artificial intelligence reduces a 100,000-equation quantum physics problem to only four equations
https://phys.org/news/2022-09-artificia ... ysics.html
by Thomas Sumner, Simons Foundation
Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.

"We start with this huge object of all these coupled-together differential equations; then we're using machine learning to turn it into something so small you can count it on your fingers," says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute's Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.

The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they're unleashed on more complex quantum systems.
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caltrek
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Superconductivity Model With 100,000 Equations Now Contains Just 4 Thanks to Artificial Intelligence
by Clare Watson
September 28, 2022

Introduction:
(Science Alert) Electrons whizzing through a grid-like lattice don't behave at all like pretty silver spheres in a pinball machine. They blur and bend in collective dances, following whims of a wave-like reality that are hard enough to imagine, let alone compute.

And yet scientists have succeeded in doing just that, capturing the motion of electrons moving about a square lattice in simulations that – until now – had required hundreds of thousands of individual equations to produce.

Using artificial intelligence (AI) to reduce that task down to just four equations, physicists have made their job of studying the emergent properties of complex quantum materials a whole lot more manageable.

In doing so, this computing feat could help tackle one of the most intractable problems of quantum physics, the 'many-electron' problem, which attempts to describe systems containing large numbers of interacting electrons.

It could also advance a truly legendary tool for predicting electron behavior in solid state materials, the Hubbard model – all the while bettering our understanding of how handy phases of matter, such as superconductivity, occur.
Read more here: https://www.sciencealert.com/supercond ... nks-to-ai

Edit: I just now noticed that Weatheriscool also posted an article on this very same topic. I will let my post stand as it is from a different media source.
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weatheriscool
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Machine learning helps scientists peer (a second) into the future
https://phys.org/news/2022-09-machine-s ... uture.html
by The Ohio State University
The past may be a fixed and immutable point, but with the help of machine learning, the future can at times be more easily divined.

Using a new type of machine learning method called next generation reservoir computing, researchers at The Ohio State University have recently found a new way to predict the behavior of spatiotemporal chaotic systems—such as changes in Earth's weather—that are particularly complex for scientists to forecast.

The study, published today in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, utilizes a new and highly efficient algorithm that, when combined with next generation reservoir computing, can learn spatiotemporal chaotic systems in a fraction of the time of other machine learning algorithms.

Researchers tested their algorithm on a complex problem that has been studied many times in the past—forecasting the behavior of an atmospheric weather model. In comparison to traditional machine learning algorithms that can solve the same tasks, the Ohio State team's algorithm is more accurate, and uses 400 to 1,250 times less training data to make better predictions than its counterpart.

Their method is also less computationally expensive; while solving complex computing problems previously required a supercomputer, they used a laptop running Windows 10 to make predictions in about a fraction of a second—about 240,000 times faster than traditional machine learning algorithms.
weatheriscool
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Dynamics in one-dimensional spin chains: A new toolbox for elucidating future quantum materials
https://phys.org/news/2022-10-dynamics- ... ating.html
by Helmholtz Association of German Research Centres

Neutron scattering is considered the method of choice for investigating magnetic structures and excitations in quantum materials. Now, for the first time, the evaluation of measurement data from the 2000s with new methods has provided much deeper insights into a model system—the 1D Heisenberg spin chains. A new toolbox for elucidating future quantum materials has been achieved.

Potassium copper fluoride (KCuF3 ) is considered the simplest model material for realizing the so-called Heisenberg quantum spin chain: The spins interact with their neighbors antiferromagnetically along a single direction (one-dimensional), governed by the laws of quantum physics.

"We carried out the measurements on this simple model material at the ISIS spallation neutron source some time ago when I was a postdoc, and we published our results in 2005, 2013 and again in 2021, comparing to new theories each time they became available," says Prof. Bella Lake, who heads the HZB-Institute Quantum Phenomena in Novel Materials. Now with new and extended methods, a team led by Prof. Alan Tennant and Dr. Allen Scheie has succeeded in gaining significantly deeper insights into the interactions between the spins and their spatial and temporal evolution.
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Stabilizing polarons opens up new physics
https://phys.org/news/2022-10-stabilizi ... ysics.html
by Papageorgiou, Ecole Polytechnique Federale de Lausanne
Physicists at EPFL have developed a formulation to solve the longstanding problem of electron self-interaction when studying polarons—quasiparticles produced by electron-phonon interactions in materials. The work can lead to unprecedented calculations of polarons in large systems, systematic studies of large sets of materials, and molecular dynamics evolving over long time periods.

One of the many peculiarities of quantum mechanics is that particles can also be described as waves. A common example is the photon, the particle associated with light.

In ordered structures, known as crystals, electrons can be seen and described as waves that spread across the entire system—a rather harmonious picture. As electrons move through the crystal, ions—atoms carrying a negative or positive charge—are periodically arranged in space.

Now, if we were to add an extra electron to the crystal, its negative charge could make the ions around it move away from their equilibrium positions. The electron charge would localize in space and couple to the surrounding structural—"lattice"—distortions of the crystal, giving rise to a new particle known as a polaron.

"Technically, a polaron is a quasi-particle, made up of an electron 'dressed' by its self-induced phonons, which represent the quantized vibrations of the crystal," says Stefano Falletta at EPFL's School of Basic Sciences. "The stability of polarons arises from a competition between two energy contributions: the gain due to charge localization, and the cost due to lattice distortions. When the polaron destabilizes, the extra electron delocalizes over the entire system, while the ions restore their equilibrium positions."
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caltrek
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Quantum Entanglement Has Now Been Directly Observed at The Macroscopic Scale
by David Nield
October 16, 2022

Introduction:
(Science Alert) Quantum entanglement is the binding together of two particles or objects, even though they may be far apart – their respective properties are linked in a way that's not possible under the rules of classical physics.

It's a weird phenomenon that Einstein described as "spooky action at a distance", but its weirdness is what makes it so fascinating to scientists. In a 2021 study, quantum entanglement was directly observed and recorded at the macroscopic scale – a scale much bigger than the subatomic particles normally associated with entanglement.

The dimensions involved are still very small from our perspective – the experiments involved two tiny aluminum drums one-fifth the width of a human hair – but in the realm of quantum physics they're absolutely huge.

"If you analyze the position and momentum data for the two drums independently, they each simply look hot," said physicist John Teufel, from the National Institute of Standards and Technology (NIST) in the US, last year.

"But looking at them together, we can see that what looks like random motion of one drum is highly correlated with the other, in a way that is only possible through quantum entanglement."
Read more here: https://www.sciencealert.com/quantum-e ... pic-scale
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