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Researchers have solved a 20-year-old problem in quantum information theory on how to calculate entanglement cost — a way to measure entanglement — in a manner that’s efficiently computable, useful, and broadly applicable in several quantum research areas. More
A typical recycling process converts large quantities of items made of a single material into more of the same. However, this approach isn’t feasible for old electronic devices, or “e-waste,” because they contain small amounts of many different materials that cannot be readily separated. Now, in ACS Omega, researchers report a selective, small-scale microrecycling strategy, which they use to convert old printed circuit boards and monitor components into a new type of strong metal coating.
In spite of the difficulty, there’s plenty of reason to recycle e-waste: It contains many potentially valuable substances that can be used to modify the performance of other materials or to manufacture new, valuable materials. Previous research has shown that carefully calibrated high temperature-based processing can selectively break and reform chemical bonds in waste to form new, environmentally friendly materials. In this way, researchers have already turned a mix of glass and plastic into valuable, silica-containing ceramics. They’ve also used this process to recover copper, which is widely used in electronics and elsewhere, from circuit boards. Based on the properties of copper and silica compounds, Veena Sahajwalla and Rumana Hossain suspected that, after extracting them from e-waste, they could combine them to create a durable new hybrid material ideal for protecting metal surfaces.
To do so, the researchers first heated glass and plastic powder from old computer monitors to 2,732 F, generating silicon carbide nanowires. They then combined the nanowires with ground-up circuit boards, put the mix on a steel substrate then heated it up again. This time the thermal transformation temperature selected was 1,832 F, melting the copper to form a silicon-carbide enriched hybrid layer atop the steel. Microscope images revealed that, when struck with a nanoscale indenter, the hybrid layer remained firmly affixed to the steel, without cracking or chipping. It also increased the steel’s hardness by 125%. The team refers to this targeted, selective microrecycling process as “material microsurgery,” and say that it has the potential to transform e-waste into advanced new surface coatings without the use of expensive raw materials.
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Materials provided by American Chemical Society. Note: Content may be edited for style and length. More
Extensive power outages and satellite blackouts that affect air travel and the internet are some of the potential consequences of massive solar storms. These storms are believed to be caused by the release of enormous amounts of stored magnetic energy due to changes in the magnetic field of the sun’s outer atmosphere — something that until now has eluded scientists’ direct measurement. Researchers believe this recent discovery could lead to better “space weather” forecasts in the future.
“We are becoming increasingly dependent on space-based systems that are sensitive to space weather. Earth-based networks and the electrical grid can be severely damaged if there is a large eruption,” says Tomas Brage, Professor of Mathematical Physics at Lund University in Sweden.
Solar flares are bursts of radiation and charged particles, and can cause geomagnetic storms on Earth if they are large enough. Currently, researchers focus on sunspots on the surface of the sun to predict possible eruptions. Another and more direct indication of increased solar activity would be changes in the much weaker magnetic field of the outer solar atmosphere — the so-called Corona.
However, no direct measurement of the actual magnetic fields of the Corona has been possible so far.
“If we are able to continuously monitor these fields, we will be able to develop a method that can be likened to meteorology for space weather. This would provide vital information for our society which is so dependent on high-tech systems in our everyday lives,” says Dr Ran Si, post-doc in this joint effort by Lund and Fudan Universities.
The method involves what could be labelled a quantum-mechanical interference. Since basically all information about the sun reaches us through “light” sent out by ions in its atmosphere, the magnetic fields must be detected by measuring their influence on these ions. But the internal magnetic fields of ions are enormous — hundreds or thousands of times stronger than the fields humans can generate even in their most advanced labs. Therefore, the weak coronal fields will leave basically no trace, unless we can rely on this very delicate effect — the interference between two “constellations” of the electrons in the ion that are close — very close — in energy.
The breakthrough for the research team was to predict and analyze this “needle in the haystack” in an ion (nine times ionized iron) that is very common in the corona.
The work is based on state-of-the art calculations performed in the Mathematical Physics division of Lund University and combined with experiments using a device that could be thought of as being able to produce and capture small parts of the solar corona — the Electron Beam Ion Trap, EBIT, in Professor Roger Hutton’s group in Fudan University in Shanghai.
“That we managed to find a way of measuring the relatively weak magnetic fields found in the outer layer of the sun is a fantastic breakthrough,” concludes Tomas Brage.
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Materials provided by Lund University. Note: Content may be edited for style and length. More
The possibility of achieving room temperature superconductivity took a tiny step forward with a recent discovery by a team of P physicists and materials scientists. More
Using a quantum computer to simulate time travel, researchers have demonstrated that, in the quantum realm, there is no “butterfly effect.” In the research, information — qubits, or quantum bits — “time travel” into the simulated past. One of them is then strongly damaged, like stepping on a butterfly, metaphorically speaking. Surprisingly, when all qubits return to the “present,” they appear largely unaltered, as if reality is self-healing.
“On a quantum computer, there is no problem simulating opposite-in-time evolution, or simulating running a process backwards into the past,” said Nikolai Sinitsyn, a theoretical physicist at Los Alamos National Laboratory and coauthor of the paper with Bin Yan, a post doc in the Center for Nonlinear Studies, also at Los Alamos. “So we can actually see what happens with a complex quantum world if we travel back in time, add small damage, and return. We found that our world survives, which means there’s no butterfly effect in quantum mechanics.”
In Ray Bradbury’s 1952 science fiction story, “A Sound of Thunder,” a character used a time machine to travel to the deep past, where he stepped on a butterfly. Upon returning to the present time, he found a different world. This story is often credited with coining the term “butterfly effect,” which refers to the extremely high sensitivity of a complex, dynamic system to its initial conditions. In such a system, early, small factors go on to strongly influence the evolution of the entire system.
Instead, Yan and Sinitsyn found that simulating a return to the past to cause small local damage in a quantum system leads to only small, insignificant local damage in the present.
This effect has potential applications in information-hiding hardware and testing quantum information devices. Information can be hidden by a computer by converting the initial state into a strongly entangled one.
“We found that even if an intruder performs state-damaging measurements on the strongly entangled state, we still can easily recover the useful information because this damage is not magnified by a decoding process,” Yan said. “This justifies talks about creating quantum hardware that will be used to hide information.”
This new finding could also be used to test whether a quantum processor is, in fact, working by quantum principles. Since the newfound no-butterfly effect is purely quantum, if a processor runs Yan and Sinitsyn’s system and shows this effect, then it must be a quantum processor.
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To test the butterfly effect in quantum systems, Yan and Sinitsyn used theory and simulations with the IBM-Q quantum processor to show how a circuit could evolve a complex system by applying quantum gates, with forwards and backwards cause and effect.
Presto, a quantum time-machine simulator.
In the team’s experiment, Alice, a favorite stand-in agent used for quantum thought experiments, prepares one of her qubits in the present time and runs it backwards through the quantum computer. In the deep past, an intruder — Bob, another favorite stand-in — meaures Alice’s qubit. This action disturbs the qubit and destroys all its quantum correlations with the rest of the world. Next, the system is run forward to the present time.
According to Ray Bradbury, Bob’s small damage to the state and all those correlations in the past should be quickly magnified during the complex forward-in-time evolution. Hence, Alice should be unable to recover her information at the end.
But that’s not what happened. Yan and Sinitsyn found that most of the presently local information was hidden in the deep past in the form of essentially quantum correlations that could not be damaged by minor tampering. They showed that the information returns to Alice’s qubit without much damage despite Bob’s interference. Counterintuitively, for deeper travels to the past and for bigger “worlds,” Alice’s final information returns to her even less damaged.
“We found that the notion of chaos in classical physics and in quantum mechanics must be understood differently,” Sinitsyn said. More
A pair of researchers at the University of California, Riverside, has used machine learning to understand what a chemical smells like — a research breakthrough with potential applications in the food flavor and fragrance industries.
“We now can use artificial intelligence to predict how any chemical is going to smell to humans,” said Anandasankar Ray, a professor of molecular, cell and systems biology, and the senior author of the study that appears in iScience. “Chemicals that are toxic or harsh in, say, flavors, cosmetics, or household products can be replaced with natural, softer, and safer chemicals.”
Humans sense odors when some of their nearly 400 odorant receptors, or ORs, are activated in the nose. Each OR is activated by a unique set of chemicals; together, the large OR family can detect a vast chemical space. A key question in olfaction is how the receptors contribute to different perceptual qualities or percepts.
“We tried to model human olfactory percepts using chemical informatics and machine learning,” Ray said. “The power of machine learning is that it is able to evaluate a large number of chemical features and learn what makes a chemical smell like, say, a lemon or a rose or something else. The machine learning algorithm can eventually predict how a new chemical will smell even though we may initially not know if it smells like a lemon or a rose.”
According to Ray, digitizing predictions of how chemicals smell creates a new way of scientifically prioritizing what chemicals can be used in the food, flavor, and fragrance industries.
“It allows us to rapidly find chemicals that have a novel combination of smells,” he said. “The technology can help us discover new chemicals that could replace existing ones that are becoming rare, for example, or which are very expensive. It gives us a vast palette of compounds that we can mix and match for any olfactory application. For example, you can now make a mosquito repellent that works on mosquitoes but is pleasant smelling to humans.”
The researchers first developed a method for a computer to learn chemical features that activate known human odorant receptors. They then screened roughly half a million compounds for new ligands — molecules that bind to receptors — for 34 odorant receptors. Next, they focused on whether the algorithm that could estimate odorant receptor activity could also predict diverse perceptual qualities of odorants.
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“Computers might help us better understand human perceptual coding, which appears, in part, to be based on combinations of differently activated ORs,” said Joel Kowalewski, a student in the Neuroscience Graduate Program working with Ray and the first author of the research paper. “We used hundreds of chemicals that human volunteers previously evaluated, selected ORs that best predicted percepts on a portion of chemicals, and tested that these ORs were also predictive of new chemicals.”
Ray and Kowalewski showed the activity of ORs successfully predicted 146 different percepts of chemicals. To their surprise, few rather than all ORs were needed to predict some of these percepts. Since they could not record activity from sensory neurons in humans, they tested this further in the fruit fly (Drosophila melanogaster) and observed a similar result when predicting the fly’s attraction or aversion to different odorants.
“If predictions are successful with less information, the task of decoding odor perception would then become easier for a computer,” Kowalewski said.
Ray explained that many items available to consumers use volatile chemicals to make themselves appealing. About 80% of what is considered flavor in food actually stems from the odors that affect smell. Fragrances for perfuming cosmetics, cleaning products, and other household goods play an important role in consumer behavior.
“Our digital approach using machine learning could open up many opportunities in the food, flavor, and fragrance industries,” he said. “We now have an unprecedented ability to find ligands and new flavors and fragrances. Using our computational approach, we can intelligently design volatile chemicals that smell desirable for use and also predict ligands for the 34 human ORs.”
The study was partially funded by UCR and the National Science Foundation. More
Scientists have developed a method paving the way to using quantum computers to simulate realistic molecules and complex materials. They tested the method on a quantum simulator and IBM quantum computer. More
Researchers used the supercomputer at Oak Ridge National Laboratory to analyze genes from cells in the lung fluid of nine COVID-19 patients compared with 40 control patients. More
