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Researchers from the University of Nebraska-Lincoln and the University of California, Berkeley, have developed a new photonic device that could get scientists closer to the “holy grail” of finding the global minimum of mathematical formulations at room temperature. Finding that illusive mathematical value would be a major advancement in opening new options for simulations involving quantum materials.

Many scientific questions depend heavily on being able to find that mathematical value, said Wei Bao, Nebraska assistant professor of electrical and computer engineering. The search can be challenging even for modern computers, especially when the dimensions of the parameters—commonly used in quantum physics—are extremely large.

Until now, researchers could only do this with polariton optimization devices at extremely low temperatures, close to about minus 270 degrees Celsius. Bao said the Nebraska-UC Berkeley team “has found a way to combine the advantages of light and matter at suitable for this great optimization challenge.”

Circa 2019


According to string theory, all particles and fundamental forces arise from the vibrational states of tiny strings. For mathematical consistency, these strings vibrate in 10-dimensional spacetime. And for consistency with our familiar everyday experience of the universe, with three spatial dimensions and the dimension of time, the additional six dimensions are “compactified” so as to be undetectable.

Different compactifications lead to different solutions. In string theory, a “solution” implies a vacuum of spacetime that is governed by Einstein’s theory of gravity coupled to a quantum field theory. Each solution describes a unique universe, with its own set of particles, fundamental forces and other such defining properties.

Some string theorists have focused their efforts on trying to find ways to connect string theory to properties of our known, observable universe—particularly the standard model of particle physics, which describes all known particles and all their mutual forces except gravity.

Marianne StebbinsWhat does this solve that isn’t already handled by air and water?

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Anne KristoffersenTurn the Bering Strait Crossing into a bridge arcology and the project will handsomely pay for itself in a sustainable way.

The Diomede Bridge ArcoCity could become a vastly important city-state, essentially having a millions-strong settlement there w… See more.

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It’s said that the clock is always ticking, but there’s a chance that it isn’t. The theory of “presentism” states that the current moment is the only thing that’s real, while “eternalism” is the belief that all existence in time is equally real. Find out if the future is really out there and predictable—just don’t tell us who wins the big game next year.

This video is episode two from the series “Mysteries of Modern Physics: Time”, Presented by Sean Carroll.
Learn more about the physics of time at https://www.wondrium.com/YouTube.

00:00 Science and Philosophy Combine When Studying Time.
2:30 Experiments Prove Continuity of Time.
6:47 Time Is Somewhat Predictable.
8:10 Why We Think of Time Differently.
8:49 Our Perception of Time Leads to Spacetime.
11:54 We Dissect Presentism vs Eternalism.
15:43 Memories and Items From the Past Make it More Real.
17:47 Galileo Discovers Pendulum Speeds Are Identical.
25:00 Thought Experiment: “What if Time Stopped?”
29:07 Time Connects Us With the Outside World.

Welcome to Wondrium on YouTube.

Here, you can enjoy a carefully curated selection of the history, science, and math videos you’ve come to know and love from brands like The Great Courses, and more.

If you’ve ever wanted to travel back in time, wondered about the science of life, wished for a better understanding of math, or dreamt of exploring the stars … then Wondrium will be your new favorite channel on YouTube!

A new molecule synthesized by a University of Texas at Dallas researcher kills a broad spectrum of hard-to-treat cancers, including triple-negative breast cancer, by exploiting a weakness in cells not previously targeted by other drugs.

A study describing the research — which was carried out in isolated cells, in human cancer tissue and in human cancers grown in mice — was published online June 2 in the journal Nature Cancer.

Dr. Jung-Mo Ahn, a co-corresponding author of the study and a UT Dallas associate professor of chemistry and biochemistry in the School of Natural Sciences and Mathematics, has been passionate about his work designing small molecules that target protein-protein interactions in cells for over a decade. Using an approach called structure-based rational drug design, he previously developed potential therapeutic candidate compounds for treatment-resistant breast cancer and for prostate cancer.

University of Queensland scientists have cracked a problem that’s frustrated chemists and physicists for years, potentially leading to a new age of powerful, efficient, and environmentally friendly technologies.

Using , Professor Ben Powell from UQ’s School of Mathematics and Physics has discovered a “recipe” which allows molecular switches to work at room temperature.

“Switches are materials that can shift between two or more states, such as on and off or 0 and 1, and are the basis of all digital technologies,” Professor Powell said. “This discovery paves the way for smaller and more powerful and energy efficient technologies. You can expect batteries will last longer and computers to run faster.”

Machine learning can get a boost from quantum physics.

On certain types of machine learning tasks, quantum computers have an exponential advantage over standard computation, scientists report in the June 10 Science. The researchers proved that, according to quantum math, the advantage applies when using machine learning to understand quantum systems. And the team showed that the advantage holds up in real-world tests.

“People are very excited about the potential of using quantum technology to improve our learning ability,” says theoretical physicist and computer scientist Hsin-Yuan Huang of Caltech. But it wasn’t entirely clear if machine learning could benefit from quantum physics in practice.