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New experimental evidence of a collective behavior of electrons to form “quasiparticles” called “anyons” has been reported by a team of scientists at Purdue University.

Anyons have characteristics not seen in other subatomic particles, including exhibiting fractional charge and fractional statistics that maintain a “memory” of their interactions with other quasiparticles by inducing quantum mechanical phase changes.

Postdoctoral research associate James Nakamura, with assistance from research group members Shuang Liang and Geoffrey Gardner, made the discovery while working in the laboratory of professor Michael Manfra is a Distinguished Professor of Physics and Astronomy, Purdue’s Bill and Dee O’Brien Chair Professor of Physics and Astronomy, professor of electrical and computer engineering, and professor of materials engineering. Although this work might eventually turn out to be relevant to the development of a quantum computer, for now, Manfra said, it is to be considered an important step in understanding the physics of quasiparticles.

The technology behind the quantum computers of the future is fast developing, with several different approaches in progress. Many of the strategies, or “blueprints,” for quantum computers rely on atoms or artificial atom-like electrical circuits. In a new theoretical study in the journal Physical Review X, a group of physicists at Caltech demonstrates the benefits of a lesser-studied approach that relies not on atoms but molecules.

“In the quantum world, we have several blueprints on the table and we are simultaneously improving all of them,” says lead author Victor Albert, the Lee A. DuBridge Postdoctoral Scholar in Theoretical Physics. “People have been thinking about using molecules to encode information since 2001, but now we are showing how molecules, which are more complex than atoms, could lead to fewer errors in quantum computing.”

At the heart of quantum computers are what are known as qubits. These are similar to the bits in classical computers, but unlike classical bits they can experience a bizarre phenomenon known as superposition in which they exist in two states or more at once. Like the famous Schrödinger’s cat thought experiment, which describes a cat that is both dead and alive at the same time, particles can exist in multiple states at once. The phenomenon of superposition is at the heart of quantum computing: the fact that qubits can take on many forms simultaneously means that they have exponentially more computing power than classical bits.

Using the spin precession of Bose-condensed 87Rb atoms as a clock, direct measurements are made of the time required for Rb atoms to quantum tunnel through a classically impenetrable barrier.

Solar flares emit sudden, strong bursts of electromagnetic radiation from the Sun’s surface and its atmosphere, and eject plasma and energetic particles into inter-planetary space. Since large solar flares can cause severe space weather disturbances affecting Earth, to mitigate their impact their occurrence needs to be predicted. However, as the onset mechanism of solar flares is unclear, most flare prediction methods so far have relied on empirical methods.

The research team led by Professor Kanya Kusano (Director of the Institute for Space-Earth Environmental Research, Nagoya University) recently succeeded in developing the first physics-based model that can accurately predict imminent large solar flares. The work was published in the journal Science on July 31, 2020.

The new method of flare prediction, called the kappa scheme, is based on the theory of “double-arc instability,” that is a magnetohydrodynamic (MHD) instability triggered by magnetic reconnection. The researchers assumed that a small-scale reconnection of magnetic field lines can form a double-arc (m-shape) magnetic field and trigger the onset of a solar flare (Figure 1). The kappa scheme can predict how a small magnetic reconnection triggers a large flare and how a large solar flare can occur.

Picture an airplane that can only climb to one or two altitudes after taking off. That limitation would be similar to the plight facing scientists who seek to avoid instabilities that restrict the path to clean, safe, and abundant fusion energy in doughnut-shaped tokamak facilities. Researchers at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) and General Atomics (GA) have now published a breakthrough explanation of this tokamak restriction and how it may be overcome.

Toroidal, or doughnut-shaped, tokamaks are prone to intense bursts of heat and particles, called edge localized modes (ELMs). These ELMs can damage the reactor walls and must be controlled to develop reliable fusion power. Fortunately, scientists have learned to tame these ELMs by applying spiraling rippled magnetic fields to the surface of the plasma that fuels fusion reactions. However, the taming of ELMs requires very specific conditions that limit the operational flexibility of tokamak reactors.

Brownian motion of particles in fluid is a common collective behavior in biological and physical systems. In a new report on Science Advances, Kai Leong Chong, and a team of researchers in physics, engineering, and aerospace engineering in China, conducted experiments and numerical simulations to show how the movement of vortices resembled inertial Brownian particles. During the experiments, the rotating turbulent convective vortical flow allowed the particles to move ballistically at first and diffusively after a critical time in a direct behavioral transition—without going through a hydrodynamic memory regime. The work implies that convective vortices have inertia-induced memory, so their short-term movement was well-defined in the framework of Brownian motion here for the first time.

Brownian motion

Albert Einstein first provided a theoretical explanation to Brownian motion in 1905 with the movement of pollen particles in a thermal bath, the phenomenon is now a common example of stochastic processes that widely occur in nature. Later in 1908, Paul Langevin noted the inertia of particles and predicted that their motion would be ballistic within a short period of time, changing to diffuse motion after a specific timeline. However, due to the rapidity of this transition, it took more than a century for researchers to be able to directly observe the phenomenon. Nevertheless, the “pure” Brownian motion predicted by Langevin was not observed in liquid systems and the transition spanned a broad range of time scales. The slow and smooth transition occurred due to the hydrodynamic memory effect, to ultimately generate long-range correlations.

Physicists’ latest achievement with neutral atoms paves the way for new quantum computer designs.

In the quest to develop quantum computers, physicists have taken several different paths. For instance, Google recently reported that their prototype quantum computer might have made a specific calculation faster than a classical computer. Those efforts relied on a strategy that involves superconducting materials, which are materials that, when chilled to ultracold temperatures, conduct electricity with zero resistance. Other quantum computing strategies involve arrays of charged or neutral atoms.

Now, a team of quantum physicists at Caltech has made strides in work that uses a more complex class of neutral atoms called the alkaline-earth atoms, which reside in the second column of the periodic table. These atoms, which include magnesium, calcium, and strontium, have two electrons in their outer regions, or shells. Previously, researchers who experimented with neutral atoms had focused on elements located in the first column of the periodic table, which have just one electron in their outer shells.

The quantum properties underlying crystal formation can be replicated and investigated with the help of ultracold atoms. A team led by Dr. Axel U. J. Lode from the University of Freiburg’s Institute of Physics has now described in the journal Physical Review Letters how the use of dipolar atoms enables even the realization and precise measurement of structures that have not yet been observed in any material. The theoretical study was a collaboration involving scientists from the University of Freiburg, the University of Vienna and the Technical University of Vienna in Austria, and the Indian Institute of Technology in Kanpur, India.

Crystals are ubiquitous in nature. They are formed by many different materials—from mineral salts to heavy metals like bismuth. Their structures emerge because a particular regular ordering of atoms or molecules is favorable, because it requires the smallest amount of energy. A cube with one constituent on each of its eight corners, for instance, is a crystal structure that is very common in nature. A crystal’s structure determines many of its physical properties, such as how well it conducts a current or heat or how it cracks and behaves when it is illuminated by light. But what determines these crystal structures? They emerge as a consequence of the quantum properties of and the interactions between their constituents, which, however, are often scientifically hard to understand and also hard measure.

To nevertheless get to the bottom of the quantum properties of the formation of crystal structures, scientists can simulate the process using Bose-Einstein condensates—trapped ultracold atoms cooled down to temperatures close to absolute zero or minus 273.15 degrees Celsius. The atoms in these highly artificial and highly fragile systems are extremely well under control.

A team of researchers with Google’s AI Quantum team (working with unspecified collaborators) has conducted the largest chemical simulation on a quantum computer to date. In their paper published in the journal Science, the group describes their work and why they believe it was a step forward in quantum computing. Xiao Yuan of Stanford University has written a Perspective piece outlining the potential benefits of quantum computer use to conduct chemical simulations and the work by the team at AI Quantum, published in the same journal issue.

Developing an ability to predict chemical processes by simulating them on computers would be of great benefit to chemists—currently, they do most of it through trial and error. Prediction would open up the door to the development of a wide range of new materials with still unknown properties. Sadly, current computers lack the exponential scaling that would be required for such work. Because of that, chemists have been hoping quantum computers will one day step in to take on the role.

Current quantum computer technology is not yet ready to take on such a challenge, of course, but computer scientists are hoping to get them there sometime in the near future. In the meantime, big companies like Google are investing in research geared toward using quantum computers once they mature. In this new effort, the team at AI Quantum focused their efforts on simulating a simple chemical process—the Hartree-Fock approximation of a real chemical system—in this particular case, a diazene molecule undergoing a reaction with hydrogen atoms, resulting in an altered configuration.