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City College of New York physicist Pouyan Ghaemi and his research team are claiming significant progress in using quantum computers to study and predict how the state of a large number of interacting quantum particles evolves over time. This was done by developing a quantum algorithm that they run on an IBM quantum computer. “To the best of our knowledge, such particular quantum algorithm which can simulate how interacting quantum particles evolve over time has not been implemented before,” said Ghaemi, associate professor in CCNY’s Division of Science.

Entitled “Probing geometric excitations of fractional quantum Hall states on quantum computers,” the study appears in the journal of Physical Review Letters.

“Quantum mechanics is known to be the underlying mechanism governing the properties of elementary particles such as electrons,” said Ghaemi. “But unfortunately there is no easy way to use equations of quantum mechanics when we want to study the properties of large number of electrons that are also exerting force on each other due to their electric charge.”

https://www.youtube.com/watch?v=z71PECJte44

What does the future of AI look like? Let’s try out some AI software that’s readily available for consumers and see how it holds up against the human brain.

🦾 AI can outperform humans. But at what cost? 👉 👉 https://cybernews.com/editorial/ai-can-outperform-humans-but-at-what-cost/

Continue reading “Watch: 🤖 🤖 Will AI become an ‘existential threat?’” »

Staff Scientist Daniele Filippetto working on the High Repetition-Rate Electron Scattering Apparatus. (Credit: Thor Swift/Berkeley Lab)

– By Will Ferguson

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Energy, mass, velocity. These three variables make up Einstein’s iconic equation E=MC². But how did Einstein know about these concepts in the first place? A precursor step to understanding physics is identifying relevant variables. Without the concept of energy, mass, and velocity, not even Einstein could discover relativity. But can such variables be discovered automatically? Doing so could greatly accelerate scientific discovery.

This is the question that researchers at Columbia Engineering posed to a new AI program. The program was designed to observe physical phenomena through a video camera, then try to search for the minimal set of fundamental variables that fully describe the observed dynamics. The study was published on July 25 in Nature Computational Science.

Continue reading “Roboticists discover alternative physics” »

Using Newtonian physics, physicists have found an expression for the value of kinetic energy, specifically KE = ½ m v^2. Einstein came up with a very different expression, specifically KE = (gamma – 1) m c^2. In this video, Fermilab’s Dr. Don Lincoln shows how these two equations are the same at low energy and how you get from one to the other.

Relativity playlist:

Continue reading “Kinetic energy: Newton vs. Einstein | Who’s right?” »

The design of protein sequences that can precisely fold into pre-specified 3D structures is a challenging task. A recently proposed deep-learning algorithm improves such designs when compared with traditional, physics-based protein design approaches.

ABACUS-R is trained on the task of predicting the AA at a given residue, using information about that residue’s backbone structure, and the backbone and AA of neighboring residues in space. To do this, ABACUS-R uses the Transformer neural network architecture⁶, which offers flexibility in representing and integrating information between different residues. Although these aspects are similar to a previous network², ABACUS-R adds auxiliary training tasks, such as predicting secondary structures, solvent exposure and sidechain torsion angles. These outputs aren’t needed during design but help with training and increase sequence recovery by about 6%. To design a protein sequence, ABACUS-R uses an iterative ‘denoising’ process (Fig.

Scientists have developed a new machine-learning platform that makes the algorithms that control particle beams and lasers smarter than ever before. Their work could help lead to the development of new and improved particle accelerators that will help scientists unlock the secrets of the subatomic world.

Daniele Filippetto and colleagues at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) developed the setup to automatically compensate for real-time changes to accelerator beams and other components, such as magnets. Their machine learning approach is also better than contemporary beam control systems at both understanding why things fail, and then using physics to formulate a response. A paper describing the research was published late last year in Nature Scientific Reports.

“We are trying to teach physics to a chip, while at the same time providing it with the wisdom and experience of a senior scientist operating the machine,” said Filippetto, a staff scientist at the Accelerator Technology & Applied Physics Division (ATAP) at Berkeley Lab and deputy director of the Berkeley Accelerator Controls and Instrumentation Program (BACI) program.

Circa 2021

Astrophysicist at Göttingen University discovers new theoretical hyper-fast soliton solutions.

If travel to distant stars within an individual’s lifetime is going to be possible, a means of faster-than-light propulsion will have to be found. To date, even recent research about superluminal (faster-than-light) transport based on Einstein’s theory of general relativity would require vast amounts of hypothetical particles and states of matter that have “exotic” physical properties such as negative energy density. This type of matter either cannot currently be found or cannot be manufactured in viable quantities. In contrast, new research carried out at the University of Göttingen gets around this problem by constructing a new class of hyper-fast ‘solitons’ using sources with only positive energies that can enable travel at any speed. This reignites debate about the possibility of faster-than-light travel based on conventional physics. The research is published in the journal Classical and Quantum Gravity.

Continue reading “Breaking the Warp Barrier for Faster-Than-Light Travel: New Theoretical Hyper-Fast Solitons Discovered” »

A new artificial intelligence algorithm called ‘Zoobot’ helped to identify 40,000 ring galaxies. What else is the astronomical AI capable of?