Computers Math

The secret to building superconducting quantum computers with massive processing power may be an ordinary telecommunications technology — optical fiber.
Physicists at the National Institute of Standards and Technology (NIST) have measured and controlled a superconducting quantum bit (qubit) using light-conducting fiber instead of metal electrical wires, paving the way to packing a million qubits into a quantum computer rather than just a few thousand. The demonstration is described in the March 25 issue of Nature.
Superconducting circuits are a leading technology for making quantum computers because they are reliable and easily mass produced. But these circuits must operate at cryogenic temperatures, and schemes for wiring them to room-temperature electronics are complex and prone to overheating the qubits. A universal quantum computer, capable of solving any type of problem, is expected to need about 1 million qubits. Conventional cryostats — supercold dilution refrigerators — with metal wiring can only support thousands at the most.
Optical fiber, the backbone of telecommunications networks, has a glass or plastic core that can carry a high volume of light signals without conducting heat. But superconducting quantum computers use microwave pulses to store and process information. So the light needs to be converted precisely to microwaves.
To solve this problem, NIST researchers combined the fiber with a few other standard components that convert, convey and measure light at the level of single particles, or photons, which could then be easily converted into microwaves. The system worked as well as metal wiring and maintained the qubit’s fragile quantum states.
“I think this advance will have high impact because it combines two totally different technologies, photonics and superconducting qubits, to solve a very important problem,” NIST physicist John Teufel said. “Optical fiber can also carry far more data in a much smaller volume than conventional cable.”
Normally, researchers generate microwave pulses at room temperature and then deliver them through coaxial metal cables to ¬¬cryogenically maintained superconducting qubits. The new NIST setup used an optical fiber instead of metal to guide light signals to cryogenic photodetectors that converted signals back to microwaves and delivered them to the qubit. For experimental comparison purposes, microwaves could be routed to the qubit through either the photonic link or a regular coaxial line. More

The heart of any computer, its central processing unit, is built using semiconductor technology, which is capable of putting billions of transistors onto a single chip. Now, researchers from the group of Menno Veldhorst at QuTech, a collaboration between TU Delft and TNO, have shown that this technology can be used to build a two-dimensional array of qubits to function as a quantum processor. Their work, a crucial milestone for scalable quantum technology, was published today in Nature.
Quantum computers have the potential to solve problems that are impossible to address with classical computers. Whereas current quantum devices hold tens of qubits — the basic building block of quantum technology — a future universal quantum computer capable of running any quantum algorithm will likely consist of millions to billions of qubits. Quantum dot qubits hold the promise to be a scalable approach as they can be defined using standard semiconductor manufacturing techniques. Veldhorst: ‘By putting four such qubits in a two-by-two grid, demonstrating universal control over all qubits, and operating a quantum circuit that entangles all qubits, we have made an important step forward in realizing a scalable approach for quantum computation.’
An entire quantum processor
Electrons trapped in quantum dots, semiconductor structures of only a few tens of nanometres in size, have been studied for more than two decades as a platform for quantum information. Despite all promises, scaling beyond two-qubit logic has remained elusive. To break this barrier, the groups of Menno Veldhorst and Giordano Scappucci decided to take an entirely different approach and started to work with holes (i.e. missing electrons) in germanium. Using this approach, the same electrodes needed to define the qubits could also be used to control and entangle them. ‘No large additional structures have to be added next to each qubit such that our qubits are almost identical to the transistors in a computer chip,’ says Nico Hendrickx, graduate student in the group of Menno Veldhorst and first author of the article. ‘Furthermore, we have obtained excellent control and can couple qubits at will, allowing us to program one, two, three, and four-qubit gates, promising highly compact quantum circuits.’
2D is key
After successfully creating the first germanium quantum dot qubit in 2019, the number of qubits on their chips has doubled every year. ‘Four qubits by no means makes a universal quantum computer, of course,’ Veldhorst says. ‘But by putting the qubits in a two-by-two grid we now know how to control and couple qubits along different directions.’ Any realistic architecture for integrating large numbers of qubits requires them to be interconnected along two dimensions.
Germanium as a highly versatile platform
Demonstrating four-qubit logic in germanium defines the state-of-the-art for the field of quantum dots and marks an important step toward dense, and extended, two-dimensional semiconductor qubit grids. Next to its compatibility with advanced semiconductor manufacturing, germanium is also a highly versatile material. It has exciting physics properties such as spin-orbit coupling and it can make contact to materials like superconductors. Germanium is therefore considered as an excellent platform in several quantum technologies. Veldhorst: ‘Now that we know how to manufacture germanium and operate an array of qubits, the germanium quantum information route can truly begin.’
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Materials science likes to take nature and the special properties of living beings that could potentially be transferred to materials as a model. A research team led by chemist Professor Andreas Walther of Johannes Gutenberg University Mainz (JGU) has succeeded in endowing materials with a bioinspired property: Wafer-thin stiff nanopaper instantly becomes soft and elastic at the push of a button. “We have equipped the material with a mechanism so that the strength and stiffness can be modulated via an electrical switch,” explained Walther. As soon as an electric current is applied, the nanopaper becomes soft; when the current flow stops, it regains its strength. From an application perspective, this switchability could be interesting for damping materials, for example. The work, which also involved scientists from the University of Freiburg and the Cluster of Excellence on “Living, Adaptive, and Energy-autonomous Materials Systems” (livMatS) funded by the German Research Foundation (DFG), was published in Nature Communications.
Inspiration from the seafloor: Mechanical switch serves a protective function
The nature-based inspiration in this case comes from sea cucumbers. These marine creatures have a special defense mechanism: When they are attacked by predators in their habitat on the seafloor, sea cucumbers can adapt and strengthen their tissue so that their soft exterior immediately stiffens. “This is an adaptive mechanical behavior that is fundamentally difficult to replicate,” said Professor Andreas Walther. With their work now published, his team has succeeded in mimicking the basic principle in a modified form using an attractive material and an equally attractive switching mechanism.
The scientists used cellulose nanofibrils extracted and processed from the cell wall of trees. Nanofibrils are even finer than the microfibers in standard paper and result in a completely transparent, almost glass-like paper. The material is stiff and strong, appealing for lightweight construction. Its characteristics are even comparable to those of aluminum alloys. In their work, the research team applied electricity to these cellulose nanofibril-based nanopapers. By means of specially designed molecular changes, the material becomes flexible as a result. The process is reversible and can be controlled by an on/off switch.
“This is extraordinary. All the materials around us are not very changeable, they do not easily switch from stiff to elastic and vice versa. Here, with the help of electricity, we can do that in a simple and elegant way,” said Walther. The development is thus moving away from classic static materials toward materials with properties that can be adaptively adjusted. This is relevant for mechanical materials, which can thus be made more resistant to fracture, or for adaptive damping materials, which could switch from stiff to compliant when overloaded, for example.
Targeting a material with its own energy storage for autonomous on/off switching
At the molecular level, the process involves heating the material by applying a current and thus reversibly breaking cross-linking points. The material softens in correlation with the applied voltage, i.e., the higher the voltage, the more cross-linking points are broken and the softer the material becomes. Professor Andreas Walther’s vision for the future also starts at the point of power supply: While currently a power source is needed to start the reaction, the next goal would be to produce a material with its own energy storage system, so that the reaction is essentially triggered “internally” as soon as, for example, an overload occurs and damping becomes necessary. “Now we still have to flip the switch ourselves, but our dream would be for the material system to be able to accomplish this on its own.”
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Have you ever wondered why you are able to hear a sentence and understand its meaning — given that the same words in a different order would have an entirely different meaning? New research involving neuroimaging and A.I., describes the complex network within the brain that comprehends the meaning of a spoken sentence.
“It has been unclear whether the integration of this meaning is represented in a particular site in the brain, such as the anterior temporal lobes, or reflects a more network level operation that engages multiple brain regions,” said Andrew Anderson, Ph.D., research assistant professor in the University of Rochester Del Monte Institute for Neuroscience and lead author on of the study which was published in the Journal of Neuroscience. “The meaning of a sentence is more than the sum of its parts. Take a very simple example — ‘the car ran over the cat’ and ‘the cat ran over the car’ — each sentence has exactly the same words, but those words have a totally different meaning when reordered.”
The study is an example of how the application of artificial neural networks, or A.I., are enabling researchers to unlock the extremely complex signaling in the brain that underlies functions such as processing language. The researchers gather brain activity data from study participants who read sentences while undergoing fMRI. These scans showed activity in the brain spanning across a network of different regions — anterior and posterior temporal lobes, inferior parietal cortex, and inferior frontal cortex. Using the computational model InferSent — an A.I. model developed by Facebook trained to produce unified semantic representations of sentences — the researchers were able to predict patterns of fMRI activity reflecting the encoding of sentence meaning across those brain regions.
“It’s the first time that we’ve applied this model to predict brain activity within these regions, and that provides new evidence that contextualized semantic representations are encoded throughout a distributed language network, rather than at a single site in the brain.”
Anderson and his team believe the findings could be helpful in understanding clinical conditions. “We’re deploying similar methods to try to understand how language comprehension breaks down in early Alzheimer’s disease. We are also interested in moving the models forward to predict brain activity elicited as language is produced. The current study had people read sentences, in the future we’re interested in moving forward to predict brain activity as people might speak sentences.”
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Materials provided by University of Rochester Medical Center. Original written by Kelsie Smith Hayduk. Note: Content may be edited for style and length. More

All viruses mutate as they make copies of themselves to spread and thrive. SARS-CoV-2, the virus the causes COVID-19, is proving to be no different. There are currently more than 4,000 variants of COVID-19, which has already killed more than 2.7 million people worldwide during the pandemic.
The UK variant, also known as B.1.1.7, was first detected in September 2020, and is now causing 98 percent of all COVID-19 cases in the United Kingdom. And it appears to be gaining a firm grip in about 100 other countries it has spread to in the past several months, including France, Denmark, and the United States.
The World Health Organization says B.1.1.7 is one of several variants of concern along with others that have emerged in South Africa and Brazil.
“The UK, South Africa, and Brazil variants are more contagious and escape immunity easier than the original virus,” said Victor Padilla-Sanchez, a research scientist at The Catholic University of America. “We need to understand why they are more infectious and, in many cases, more deadly.”
All three variants have undergone changes to their spike protein — the part of the virus which attaches to human cells. As a result, they are better at infecting cells and spreading.
In a research paper published in January 2021 in Research Ideas and Outcomes, Padilla-Sanchez discusses the UK and South African variants in detail. He presents a computational analysis of the structure of the spike glycoprotein bound to the ACE2 receptor where the mutations have been introduced. His paper outlines the reason why these variants bind better to human cells. More

NIMS and the Tokyo Institute of Technology have jointly discovered that the chemical compound Ca3SiO is a direct transition semiconductor, making it a potentially promising infrared LED and infrared detector component. This compound — composed of calcium, silicon and oxygen — is cheap to produce and non-toxic. Many of the existing infrared semiconductors contain toxic chemical elements, such as cadmium and tellurium. Ca3SiO may be used to develop less expensive and safer near-infrared semiconductors.
Infrared wavelengths have been used for many purposes, including optical fiber communications, photovoltaic power generation and night vision devices. Existing semiconductors capable of emitting infrared radiation (i.e., direct transition semiconductors) contain toxic chemical compounds, such as mercury cadmium telluride and gallium arsenide. Infrared semiconductors free of toxic chemical elements are generally incapable of emitting infrared radiation (i.e., indirect transition semiconductors). It is desirable to develop high-performance infrared devices using non-toxic, direct transition semiconductors with a band gap in the infrared range.
Conventionally, the semiconductive properties of materials, such as energy band gap, have been controlled by combining two chemical elements that are located on the left and right side of group IV elements, such as III and V or II and VI. In this conventional strategy, energy band gap becomes narrower by using heavier elements: consequently, this strategy has led to the development of direct transition semiconductors composed of toxic elements, such as mercury cadmium telluride and gallium arsenide. To discover infrared semiconductors free of toxic elements, this research group took an unconventional approach: they focused on crystalline structures in which silicon atoms behave as tetravalent anions rather than their normal tetravalent cation state. The group ultimately chose oxysilicides (e.g., Ca3SiO) and oxygermanides with an inverse perovskite crystalline structure, synthesized them, evaluated their physical properties and conducted theoretical calculations. These processes revealed that these compounds exhibit a very small band gap of approximately 0.9 eV at a wavelength of 1.4 ?m, indicating their great potential to serve as direct transition semiconductors. These compounds with a small direct band gap may potentially be effective in absorbing, detecting and emitting long infrared wavelengths even when they are processed into thin films, making them very promising near-infrared semiconductor materials to be used in infrared sources (e.g., LEDs) and detectors.
In future research, we plan to develop high-intensity infrared LEDs and highly sensitive infrared detectors by synthesizing these compounds in the form of large single-crystals, developing thin film growth processes and controlling their physical properties through doping and transforming them into solid solutions. If these efforts bear fruit, toxic chemical elements currently used in existing near-infrared semiconductors may be replaced with non-toxic ones.
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Researchers at Chalmers University of Technology, Gothenburg, Sweden, have developed a novel type of thermometer that can simply and quickly measure temperatures during quantum calculations with extremely high accuracy. The breakthrough provides a benchmarking tool for quantum computing of great value — and opens up for experiments in the exciting field of quantum thermodynamics.
A key component in quantum computers are coaxial cables and waveguides — structures which guide waveforms, and act as the vital connection between the quantum processor, and the classical electronics which control it. Microwave pulses travel along the waveguides to the quantum processor, and are cooled down to extremely low temperatures along the way. The waveguide also attenuates and filters the pulses, enabling the extremely sensitive quantum computer to work with stable quantum states.
In order to have maximum control over this mechanism, the researchers need to be sure that these waveguides are not carrying noise due to thermal motion of electrons on top of the pulses that they send. In other words, they have to measure the temperature of the electromagnetic fields at the cold end of the microwave waveguides, the point where the controlling pulses are delivered to the computer’s qubits. Working at the lowest possible temperature minimises the risk of introducing errors in the qubits.
Until now, researchers have only been able to measure this temperature indirectly, with relatively large delay. Now, with the Chalmers researchers’ novel thermometer, very low temperatures can be measured directly at the receiving end of the waveguide — very accurately and with extremely high time resolution.
“Our thermometer is a superconducting circuit, directly connected to the end of the waveguide being measured. It is relatively simple — and probably the world’s fastest and most sensitive thermometer for this particular purpose at the millikelvin scale,” says Simone Gasparinetti, Assistant Professor at the Quantum Technology Laboratory, Chalmers University of Technology.
Important for measuring quantum computer performance
The researchers at the Wallenberg Centre for Quantum Technology, WACQT, have the goal to build a quantum computer — based on superconducting circuits — with at least 100 well-functioning qubits, performing correct calculations by 2030. It requires a processor working temperature close to absolute zero, ideally down to 10 millikelvin. The new thermometer gives the researchers an important tool for measuring how good their systems are and what shortcomings exist — a necessary step to be able to refine the technology and achieve their goal. More

Army-funded researchers demonstrated a machine learning approach that corrects quantum information in systems composed of photons, improving the outlook for deploying quantum sensing and quantum communications technologies on the battlefield.
When photons are used as the carriers of quantum information to transmit data, that information is often distorted due to environment fluctuations destroying the fragile quantum states necessary to preserve it.
Researchers from Louisiana State University exploited a type of machine learning to correct for information distortion in quantum systems composed of photons. Published in Advanced Quantum Technologies, the team demonstrated that machine learning techniques using the self-learning and self-evolving features of artificial neural networks can help correct distorted information. This results outperformed traditional protocols that rely on conventional adaptive optics.
“We are still in the fairly early stages of understanding the potential for machine learning techniques to play a role in quantum information science,” said Dr. Sara Gamble, program manager at the Army Research Office, an element of U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory. “The team’s result is an exciting step forward in developing this understanding, and it has the potential to ultimately enhance the Army’s sensing and communication capabilities on the battlefield.”
For this research, the team used a type of neural network to correct for distorted spatial modes of light at the single-photon level.
“The random phase distortion is one of the biggest challenges in using spatial modes of light in a wide variety of quantum technologies, such as quantum communication, quantum cryptography, and quantum sensing,” said Narayan Bhusal, doctoral candidate at LSU. “Our method is remarkably effective and time-efficient compared to conventional techniques. This is an exciting development for the future of free-space quantum technologies.”
According to the research team, this smart quantum technology demonstrates the possibility of encoding of multiple bits of information in a single photon in realistic communication protocols affected by atmospheric turbulence.
“Our technique has enormous implications for optical communication and quantum cryptography,” said Omar Magaña Loaiza, assistant professor of physics at LSU. “We are now exploring paths to implement our machine learning scheme in the Louisiana Optical Network Initiative to make it smart, more secure, and quantum.”
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