Quantum computers, systems that process information leveraging quantum mechanical effects, are expected to outperform classical computers on some complex tasks. Over the past few decades, many physicists and quantum engineers have tried to demonstrate the advantages of quantum systems over their classical counterparts on specific types of computations.
Researchers at Autonomous University of Barcelona and Hunter College of CUNY recently showed that quantum systems could tackle a problem that cannot be solved by classical systems, namely determining the even or odd nature of particle permutations without marking all and each one of the particles with a distinct label. This task essentially entails uncovering whether re-arranging particles from their original order to a new order requires an even or odd number of swaps in the position of particle pairs.
These researchers have been conducting research focusing on problems that entail the discrimination between quantum states for several years. Their recent paper, published in Physical Review Letters, demonstrates that quantum technologies could solve one of these problems in ways that are unfeasible for classical systems.
“We saw some peculiar brain activity in the model,” Miller says. “There was a group of neurons that predicted the wrong answer, yet they kept getting stronger as the model learned. So we went back to the original macaque data, and the same signal was there, hiding in plain sight. It wasn’t a quirk of the model — the monkeys’ brains were doing it too. Even as their performance improved, both the real and simulated brains maintained a reserve of neurons that continued to predict the incorrect answer.”
The new work, published in Nature Communications, puts a name to these overlooked signals: incongruent neurons, or ICNs, and explores theories as to why a primate brain might want to keep alternate options in mind, even if they’re not the right ones at the moment.
Beyond identifying a previously unrecognized class of neurons involved in learning, the study shows that the model behaves like a brain and generates realistic brain activity, even without being trained on neural data. The findings could have major implications for testing potential neurological drugs and for using computational models to investigate how cognition emerges and functions.
A new transceiver invented by electrical engineers at the University of California, Irvine boosts radio frequencies into 140-gigahertz territory, unlocking data speeds that rival those of physical fiber-optic cables and laying the groundwork for a transition to 6G and FutureG data transmission protocols.
To create the transceiver, researchers in UC Irvine’s Samueli School of Engineering devised a unique architecture that blends digital and analog processing. The result is a silicon chip system, comprising both a transmitter and a receiver, that’s capable of processing digital signals significantly faster and with much greater energy efficiency than previously available technologies.
The team from UC Irvine’s Nanoscale Communication Integrated Circuits Labs outline its work in two papers published this month in the IEEE Journal of Solid-State Circuits. In one, the engineers discuss the technology they call a “bits-to-antenna” transmitter, and in the second, they cover their “antenna-to-bits” receiver.
In inertial confinement fusion, a capsule of fuel begins at temperatures near zero and pressures close to vacuum. When lasers compress that fuel to trigger fusion, the material heats up to millions of degrees and reaches pressures similar to the core of the sun. That process happens within a miniscule amount of space and time.
To understand this process, scientists need to know about the large-scale conditions, like temperature and pressure, throughout the target chamber. But they also want detailed information about the material—and the atoms—contained within. Until now, computer models have struggled to bridge that gap across the wide range of conditions encountered in such experiments.
Quantum computers, systems that process information leveraging quantum mechanical effects, could reliably tackle various computational problems that cannot be solved by classical computers. These systems process information in the form of qubits, units of information that can exist in two states at once (0 and 1).
Hole spins, the intrinsic angular momentum of holes (i.e., missing electrons in semiconductors that can be trapped in nanoscale regions called quantum dots), have been widely used as qubits. These spins can be controlled using electric fields, as they are strongly influenced by a quantum effect known as spin-orbit coupling, which links the motion of particles to their magnetism.
Unfortunately, due to this spin-orbit coupling, hole spin qubits are also known to be highly vulnerable to noise, including random electrical disturbances that can prompt decoherence. This in turn can result in the loss of valuable quantum information.
Placental toxicology progress!
Commonly used in vitro and in vivo placental models capture key placental functions and toxicity mechanisms, but have significant limitations.
The physiological relevance of placental models varies, with a general hierarchy of simple in vitro complex in vitro/ organ-on-chip in vivo, but species-of origin considerations may alter their relevance to human physiology.
Cellular, rodent, human, and computational modeling systems provide insights into placental transport, physiology, and toxicology linked to maternal–fetal health.
Recent advances in 3D culture and microfluidic technologies offer more physiologically relevant models for studying the placenta.
Mathematical modeling approaches can integrate mechanistic physiological data and exposure assessments to define key toxicokinetic parameters.
Environmental chemical concentrations and omic data obtained from placental tissues can link toxicant influences on placental function to adverse birth outcomes.
“Our study shows that, depending on the interior properties and the softness of the bottom of the vortex, this will influence the kind of fluid pattern you observe at the surface,” said Dr. Wanying Kang.
What processes are responsible for shaping Jupiter and Saturn’s polar weather? This is what a recent study published in the Proceedings of the National Academy of Sciences hopes to address as a team of scientists from the Massachusetts Institute of Technology (MIT) investigated how the polar vortex structures on Jupiter and Saturn could provide key insight into the interiors of both planets. This study has the potential to help scientists better understand the complex processes on gas giant planets, which could serve as analogs for gas giant exoplanets.
For the study, the researchers used a series of computer models to simulate how the vortex patterns on Jupiter and Saturn are produced. The motivation for this study comes from several years of spacecraft images and observations that clearly show both planets exhibiting very different polar vortex patterns. Until now, researchers have been stumped regarding the processes responsible for two different patterns on each planet. In the end, the researchers discovered that the planet’s interior composition is responsible for the polar vortex patterns. For example, Jupiter’s interior is comprised of light materials, resulting in a large area of smaller vortices. In contrast, Saturn’s interior is comprised of denser materials, resulting in one large vortex.