Aurelia Butler

Quantum bits (qubits) are the smallest units of information in a quantum computer. Currently, one of the biggest challenges in developing this kind of powerful computer is scalability. A research group at the University of Basel, working with the IBM Research Laboratory in Rüschlikon, has made a breakthrough in this area.
Quantum computers promise unprecedented computing power, but to date prototypes have been based on just a handful of computing units. Exploiting the potential of this new generation of computers requires combining large quantities of qubits.
It is a scalability problem which once affected classic computers, as well; in that case it was solved with transistors integrated into silicon chips. The research team led by Dr. Andreas Kuhlmann and Professor Dominik Zumbühl from the University of Basel has now come up with silicon-based qubits that are very similar in design to classic silicon transistors. The researchers published their findings in the journal Nature Electronics.
Building on classic silicon technology
In classic computers, the solution to the scalability problem lay in silicon chips, which today include billions of “fin field-effect transistors” (FinFETs). These FinFETs are small enough for quantum applications; at very low temperatures near absolute zero (0 kelvin or -273.15 degrees Celsius), a single electron with a negative charge or a “hole” with a positive charge can act as a spin qubit. Spin qubits store quantum information in the two states spin-up (intrinsic angular momentum up) and spin-down (intrinsic angular momentum down).
The qubits developed by Kuhlmann’s team are based on FinFET architecture and use holes as spin qubits. In contrast with electron spin, hole spin in silicon nanostructures can be directly manipulated with fast electrical signals.
Potential for higher operating temperatures
Another major obstacle to scalability is temperature; previous qubit systems typically had to operate at an extremely low range of about 0.1 kelvin. Controlling each qubit requires additional measuring lines to connect the control electronics at room temperature to the qubits in the cryostat — a cooling unit which generates extremely low temperatures. The number of these measuring lines is limited because each line produces heat. This inevitably creates a bottleneck in the wiring, which in turn sets a limit to scaling.
Circumventing this “wiring bottleneck” is one of the main goals of Kuhlmann’s research group, and requires measurement and control electronics to be built directly into the cooling unit. “However, integrating these electronics requires qubit operation at temperatures above 1 kelvin, with the cooling power of the cryostats increasing sharply to compensate for the heat dissipation of the control electronics,” explains Dr. Leon Camenzind of the Department of Physics at the University of Basel. Doctoral student Simon Geyer, who shares lead authorship of the study with Camenzind, adds, “We have overcome the 4 kelvin-mark with our qubits, reaching the boiling point of liquid helium. Here we can achieve much greater cooling power, which allows for integration of state-of-the-art cryogenic control technology.”
Close to industry standards
Working with proven technology such as FinFET architecture to build a quantum computer offers the potential for scaling up to very large numbers of qubits. “Our approach of building on existing silicon technology puts us close to industry practice,” says Kuhlmann. The samples were created at the Binnig and Rohrer Nanotechnology Center at the IBM Research Zurich laboratory in Rüschlikon, a partner of the NCCR SPIN, which is based at the University of Basel and counts the research team as a member.
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You can carry an entire computer in your pocket today because the technological building blocks have been getting smaller and smaller since the 1950s. But in order to create future generations of electronics — such as more powerful phones, more efficient solar cells, or even quantum computers — scientists will need to come up with entirely new technology at the tiniest scales.
One area of interest is nanocrystals. These tiny crystals can assemble themselves into many configurations, but scientists have had trouble figuring out how to make them talk to each other.
A new study introduces a breakthrough in making nanocrystals function together electronically. Published March 25 in Science, the research may open the doors to future devices with new abilities.
“We call these super atomic building blocks, because they can grant new abilities — for example, letting cameras see in the infrared range,” said University of Chicago Prof. Dmitri Talapin, the corresponding author of the paper. “But until now, it has been very difficult to both assemble them into structures and have them talk to each other. Now for the first time, we don’t have to choose. This is a transformative improvement.”
In their paper, the scientists lay out design rules which should allow for the creation of many different types of materials, said Josh Portner, a Ph.D. student in chemistry and one of the first authors of the study.
A tiny problem
Scientists can grow nanocrystals out of many different materials: metals, semiconductors, and magnets will each yield different properties. But the trouble was that whenever they tried to assemble these nanocrystals together into arrays, the new supercrystals would grow with long “hairs” around them. More

Newly discovered Fermi arcs that can be controlled through magnetism could be the future of electronics based on electron spins. These new Fermi arcs were discovered by a team of researchers from Ames Laboratory and Iowa State University, as well as collaborators from the United States, Germany, and the United Kingdom. During their investigation of the rare-earth monopnictide NdBi (neodymium-bismuth), the research team discovered a new type of Fermi arc that appeared at low temperatures when the material became antiferromagnetic, i.e., neighboring spins point in opposite directions.
Fermi surfaces in metals are a boundary between energy states that are occupied and unoccupied by electrons. Fermi surfaces are normally closed contours forming shapes such as spheres, ovoids, etc. Electrons at the Fermi surface control many properties of materials such as electrical and thermal conductivity, optical properties, etc. In extremely rare occasions, the Fermi surface contains disconnected segments that are known as Fermi arcs and often are associated with exotic states like superconductivity.
Adam Kaminski, leader of the research team, explained that newly discovered Fermi arcs are the result of electron band splitting, which results from the magnetic order of Nd atoms that make up 50% of the sample. However, the electron splitting that the team observed in NdBi was not typical band splitting behavior.
There are two established types of band splitting, Zeeman and Rashba. In both cases the bands retain their original shape after splitting. The band splitting that the research team observed resulted in two bands of different shapes. As the temperature of the sample decreased, the separation between these bands increased and the band shapes changed, indicating a change in fermion mass.
“This splitting is very, very unusual, because not only is the separation between those bands increasing, but they also change the curvature,” Kaminski said. “This is very different from anything else that people have observed to date.”
The previously known cases of Fermi arcs in Weyl semimetals persist because they are caused by the crystal structure of the material which is difficult to control. However, the Fermi arcs that the team discovered in NdBi are induced by magnetic ordering of the Nd atoms in the sample. This order can be readily changed by applying a magnetic field, and possibly by changing the Nd ion for another rare earth ion such as Cerium, Praseodymium, or Samarium (Ce, Pr, or Sm). Since Ames Lab is a world leader in rare earth research, such changes in composition can be easily explored. More

Researchers at the University of Bonn have created a gas of light particles that can be extremely compressed. Their results confirm the predictions of central theories of quantum physics. The findings could also point the way to new types of sensors that can measure minute forces. The study is published in the journal Science.
If you plug the outlet of an air pump with your finger, you can still push its piston down. The reason: Gases are fairly easy to compress — unlike liquids, for example. If the pump contained water instead of air, it would be essentially impossible to move the piston, even with the greatest effort.
Gases usually consist of atoms or molecules that swirl more or less quickly through space. It is quite similar with light: Its smallest building blocks are photons, which in some respect behave like particles. And these photons can also be treated as a gas, however, one that behaves somewhat unusually: You can compress it under certain conditions with almost no effort. At least that is what theory predicts.
Photons in the mirror box
Researchers from the Institute of Applied Physics (IAP) at the University of Bonn have now demonstrated this very effect in experiments for the first time. “To do this, we stored light particles in a tiny box made of mirrors,” explains Dr. Julian Schmitt of the IAP, who is a principal investigator in the group of Prof. Dr. Martin Weitz. “The more photons we put in there, the denser the photon gas became.”
The rule is usually: The denser a gas, the harder it is to compress. This is also the case with the plugged air pump — at first the piston can be pushed down very easily, but at some point it can hardly be moved any further, even when applying a lot of force. The Bonn experiments were initially similar: The more photons they put into the mirror box, the more difficult it became to compress the gas. More

In recent years, artificial intelligence has become ubiquitous, with applications such as speech interpretation, image recognition, medical diagnosis, and many more. At the same time, quantum technology has been proven capable of computational power well beyond the reach of even the world’s largest supercomputer. Physicists at the University of Vienna have now demonstrated a new device, called quantum memristor, which may allow to combine these two worlds, thus unlocking unprecedented capabilities. The experiment, carried out in collaboration with the National Research Council (CNR) and the Politecnico di Milano in Italy, has been realized on an integrated quantum processor operating on single photons. The work is published in the current issue of the journal Nature Photonics.
At the heart of all artificial intelligence applications are mathematical models called neural networks. These models are inspired by the biological structure of the human brain, made of interconnected nodes. Just like our brain learns by constantly rearranging the connections between neurons, neural networks can be mathematically trained by tuning their internal structure until they become capable of human-level tasks: recognizing our face, interpreting medical images for diagnosis, even driving our cars. Having integrated devices capable of performing the computations involved in neural networks quickly and efficiently has thus become a major research focus, both academic and industrial.
One of the major game changers in the field was the discovery of the memristor, made in 2008. This device changes its resistance depending on a memory of the past current, hence the name memory-resistor, or memristor. Immediately after its discovery, scientists realized that (among many other applications) the peculiar behavior of memristors was surprisingly similar to that of neural synapses. The memristor has thus become a fundamental building block of neuromorphic architectures.
A group of experimental physicists from the University of Vienna, the National Research Council (CNR) and the Politecnico di Milano led by Prof. Philip Walther and Dr. Roberto Osellame, have now demonstrated that it is possible to engineer a device that has the same behavior as a memristor, while acting on quantum states and being able to encode and transmit quantum information. In other words, a quantum memristor. Realizing such device is challenging because the dynamics of a memristor tends to contradict the typical quantum behavior.
By using single photons, i.e. single quantum particles of lights, and exploiting their unique ability to propagate simultaneously in a superposition of two or more paths, the physicists have overcome the challenge. In their experiment, single photons propagate along waveguides laser-written on a glass substrate and are guided on a superposition of several paths. One of these paths is used to measure the flux of photons going through the device and this quantity, through a complex electronic feedback scheme, modulates the transmission on the other output, thus achieving the desired memristive behavior. Besides demonstrating the quantum memristor, the researchers have provided simulations showing that optical networks with quantum memristor can be used to learn on both classical and quantum tasks, hinting at the fact that the quantum memristor may be the missing link between artificial intelligence and quantum computing.
“Unlocking the full potential of quantum resources within artificial intelligence is one of the greatest challenges of the current research in quantum physics and computer science,” says Michele Spagnolo, who is first author of the publication in the journal “Nature Photonics.” The group of Philip Walther of the University of Vienna has also recently demonstrated that robots can learn faster when using quantum resources and borrowing schemes from quantum computation. This new achievement represents one more step towards a future where quantum artificial intelligence become reality.
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Researchers have developed a new analog photonic correlator that can be used to locate an object transmitting a radio signal. Because the new correlator is faster than other methods and works with a wide range of radio frequency signals, it could be useful for locating cell phones, signal jammers or a variety of tracking tags.
“The photonic architecture we developed uses no moving parts and enables real-time signal processing,” said Hugues Guillet de Chatellus from Université Grenoble Alpes-CNRS in France. “Real-time processing helps ensure there isn’t any downtime, which is critical for defense applications, for example.”
In Optica, Optica Publishing Group’s journal for high-impact research, Guillet de Chatellus and colleagues describe the new photonic correlator and demonstrate its ability to identify the location of a radio frequency transmitter. The device is considerably simpler than today’s analog or digital correlators and uses off-the-shelf telecommunications components.
“Many of today’s radio signals have large bandwidths because they carry a great deal of information,” said Guillet de Chatellus. “Our photonic approach offers a simple method for correlating signals with bandwidths of up to a few GHz, a larger bandwidth than is available from commercial approaches based on purely digital techniques.”
Using light to calculate correlation
The new photonic correlator can be used to compute what is known as a cross-correlation function for two signals emitted from one source and detected by two antennas. This measures the similarity of the signals as a function of the displacement of one signal relative to the other and provides information about their relative delay, which can be used to calculate the location of the signal’s source. More

If a sticker on a banana can make it show up as a toaster, how might strategic vandalism warp how an autonomous vehicle perceives a stop sign? Now, an immune-inspired defense system for neural networks can ward off such attacks, designed by engineers, biologists and mathematicians at the University of Michigan.
Deep neural networks are a subset of machine learning algorithms used for a wide variety of classification problems. These include image identification and machine vision (used by autonomous vehicles and other robots), natural language processing, language translation and fraud detection. However, it is possible for a nefarious person or group to adjust the input slightly and send the algorithm down the wrong train of thought, so to speak. To protect algorithms against such attacks, the Michigan team developed the Robust Adversarial Immune-inspired Learning System.
“RAILS represents the very first approach to adversarial learning that is modeled after the adaptive immune system, which operates differently than the innate immune system,” said Alfred Hero, the John H. Holland Distinguished University Professor, who co-led the work published in IEEE Access.
While the innate immune system mounts a general attack on pathogens, the mammalian immune system can generate new cells designed to defend against specific pathogens. It turns out that deep neural networks, already inspired by the brain’s system of information processing, can take advantage of this biological process, too.
“The immune system is built for surprises,” said Indika Rajapakse, associate professor of computational medicine and bioinformatics and co-leader of the study. “It has an amazing design and will always find a solution.”
RAILS works by mimicking the natural defenses of the immune system to identify and ultimately take care of suspicious inputs to the neural network. To begin developing it, the biological team studied how the adaptive immune systems of mice responded to an antigen. The experiment used the tissues of genetically modified mice that express fluorescent markers on their B cells. More