Computers Math

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.

advertisement

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.

advertisement

“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

When the Shewanella oneidensis bacterium ‘breathes’ in certain metal and sulfur compounds anaerobically, the way an aerobic organism would process oxygen, it produces materials that could be used to enhance electronics, electrochemical energy storage, and drug-delivery devices. The ability of this bacterium to produce molybdenum disulfide — a material that is able to transfer electrons easily, like graphene — is the focus of new research. More

New research shows that selecting a targeted retirement fund that ends in a zero could negatively impact your retirement savings. The study identified a ”zero bias” or tendency for individuals to select retirement funds ending in zero, which affects the amount people contribute to retirement savings and leads to an investment portfolio with an incompatible level of risk. More

Researchers designed a wearable bracelet using sound augmentation to leverage play benefits by employing digital technology. The research team also investigated how sound influences children’s play experiences according to their physical, social, and imaginative aspects. More