To begin with, it will only run at a fraction of its eventual three-quadrillion-watt maximum output.
A laser at the University of Michigan, which is set to be the most powerful in the United States, is preparing to send its first laser pulses into an experimental target, a press statement explains.
New research reveals that spinning quasiparticles, or magnons, light up when paired with a light-emitting quasiparticle, or exciton, with potential quantum information applications.
All magnets contain spinning quasiparticles called magnons. This is true of all magnets from the simple souvenirs hanging on your refrigerator to the discs that give your computer memory storage to the powerful versions used in research labs. The direction one magnon spins can influence that of its neighbor, which in turn affects the spin of its neighbor, and so on, yielding what are known as spin waves. Spin waves can potentially transmit information more efficiently than electricity, and magnons can serve as “quantum interconnects” that “glue” quantum bits together into powerful computers.
Although magnons have enormous potential, they are often difficult to detect without bulky pieces of lab equipment. According to Columbia researcher Xiaoyang Zhu, such setups are fine for conducting experiments, but not for developing devices, such as magnonic devices and so-called spintronics. However, seeing magnons can be made much simpler with the right material: a magnetic semiconductor called chromium sulfide bromide (CrSBr) that can be peeled into atom.
The equations of quantum mechanics provide a roadmap to predicting the properties of chemicals starting from basic scientific theories. However, these equations quickly become too expensive in terms of computer time and power when used to predict behavior in large systems. Machine learning offers a promising approach to accelerating such large-scale simulations.
Researchers have shown that machine learning models can mimic the basic structure of the fundamental laws of nature. These laws can be very difficult to simulate directly. The machine learning approach enables predictions that are easy to compute and are accurate in a wide range of chemical systems.
The improved machine learning model can quickly and accurately predict a wide range of properties of molecules (Proceedings of the National Academy of Sciences, “Deep Learning of Dynamically Responsive Chemical Hamiltonians with Semi-Empirical Quantum Mechanics”). These approaches score very well on important benchmarks in computational chemistry and show how deep learning methods can continue to improve by incorporating more data from experiments. The model can also succeed at challenging tasks such as predicting excited state dynamics—how systems behave with elevated energy levels.
Two big players in computing and research are trying to lay the groundwork for a future quantum internet.
Amazon Web Services (AWS) is teaming up with Harvard University to test and develop strategies for networking together quantum technologies. Their partnership was announced today, and is a continuation of AWS’ goals to create a communications channel between the quantum computers that it is also working on in parallel.
During the three-year research alliance, funding from Amazon will support research projects at Harvard that focus on quantum memory, integrated photonics, and quantum materials, and help upgrade infrastructure in Harvard’s Center for Nanoscale Systems.
If successful it would revolutionize battlefield surveillance, night vision, and terrestrial & space imaging plus many commercial applications: https://www.darpa.mil/news-events/2022-09-02
American physicist, professor of theoretical physics at Stanford University, and founding director of the Stanford Institute for Theoretical Physics, Leonard Susskind, explains black holes, quantum physics, general relativity and how they are intertwined.
Knowing how the laws of physics behave at the extremes of space and time, near a black hole, is an important piece of the puzzle we must obtain if we are to understand how the universe works. Leonard Susskind explains how general relativity and quantum mechanics are related.
Continue reading “Leonard Susskind Marrying Quantum Physics & General Relativity” »
A team of researchers from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and Stony Brook University have devised a new quantum algorithm to compute the lowest energies of molecules at specific configurations during chemical reactions, including when their chemical bonds are broken. As described in Physical Review Research, compared to similar existing algorithms, including the team’s previous method, the new algorithm will significantly improve scientists’ ability to accurately and reliably calculate the potential energy surface in reacting molecules.
For this work, Deyu Lu, a Center for Functional Nanomaterials (CFN) physicist at Brookhaven Lab, worked with Tzu-Chieh Wei, an associate professor specializing in quantum information science at the C.N. Yang Institute for Theoretical Physics at Stony Brook University, Qin Wu, a theorist at CFN, and Hongye Yu, a Ph.D. student at Stony Brook.
“Understanding the quantum mechanics of a molecule, how it behaves at an atomic level, can provide key insight into its chemical properties, like its stability and reactivity,” said Lu.
In our common experience, you can’t get something for nothing. In the quantum realm, something really can emerge from nothing.
The activity of neurons has been measured in a slice of mouse tissue using a quantum diamond sensor – and it might one day enable a new type of non-invasive brain scanning.
Few things in the Universe keep the beat as reliably as an atom’s pulse.
Yet even the most advanced ‘atomic’ clocks based on variations of these quantum timekeepers lose count when pushed to their limits.
Physicists have known for some time that entangling atoms can help tie particles down enough to squeeze a little more tick from every tock, yet most experiments have only been able to demonstrate this on the smallest of scales.
