Computers Math

To the human eye, most stationary objects appear to be just that — still, and completely at rest. Yet if we were handed a quantum lens, allowing us to see objects at the scale of individual atoms, what was an apple sitting idly on our desk would appear as a teeming collection of vibrating particles, very much in motion.
In the last few decades, physicists have found ways to super-cool objects so that their atoms are at a near standstill, or in their “motional ground state.” To date, physicists have wrestled small objects such as clouds of millions of atoms, or nanogram-scale objects, into such pure quantum states.
Now for the first time, scientists at MIT and elsewhere have cooled a large, human-scale object to close to its motional ground state. The object isn’t tangible in the sense of being situated at one location, but is the combined motion of four separate objects, each weighing about 40 kilograms. The “object” that the researchers cooled has an estimated mass of about 10 kilograms, and comprises about 1×1026, or nearly 1 octillion, atoms.
The researchers took advantage of the ability of the Laser Interfrometer Gravitational-wave Observatory (LIGO) to measure the motion of the masses with extreme precision and super-cool the collective motion of the masses to 77 nanokelvins, just shy of the object’s predicted ground state of 10 nanokelvins.
Their results, appearing today in Science, represent the largest object to be cooled to close to its motional ground state. The scientists say they now have a chance to observe the effect of gravity on a massive quantum object.
“Nobody has ever observed how gravity acts on massive quantum states,” says Vivishek Sudhir, assistant professor of mechanical engineering at MIT, who directed the project. “We’ve demonstrated how to prepare kilogram-scale objects in quantum states. This finally opens the door to an experimental study of how gravity might affect large quantum objects, something hitherto only dreamed of.”
The study’s authors are members of the LIGO Laboratory, and include lead author and graduate student Chris Whittle, postdoc Evan Hall, research scientist Sheila Dwyer, Dean of the School of Science and the Curtis and Kathleen Marble Professor of Astrophysics Nergis Mavalvala, and assistant professor of mechanical engineering Vivishek Sudhir. More

Using neural networks, researchers from the University of Copenhagen have developed a new method to search the human genome for beneficial mutations from Neanderthals and other archaic humans. These humans are known to have interbred with modern humans, but the overall fate of the genetic material inherited from them is still largely unknown. Among others, the researchers found previously unreported mutations involved in core pathways in metabolism, blood-related diseases and immunity.
Thousands of years ago, archaic humans such as Neanderthals and Denisovans went extinct. But before that, they interbred with the ancestors of present-day humans, who still to this day carry genetic mutations from the extinct species.
Over 40 percent of the Neanderthal genome is thought to have survived in different present-day humans of non-African descent, but spread out so that any individual genome is only composed of up to two percent Neanderthal material. Some human populations also carry genetic material from Denisovans — a mysterious group of archaic humans that may have lived in Eastern Eurasia and Oceania thousands of years ago.
The introduction of beneficial genetic material into our gene pool, a process known as adaptive introgression, often happened because it was advantageous to humans after they expanded across the globe. To name a few examples, scientists believe some of the mutations affected skin development and metabolism. But many mutations are yet still undiscovered.
Now, researchers from GLOBE Institute at the University of Copenhagen have developed a new method using deep learning techniques to search the human genome for undiscovered mutations.
“We developed a deep learning method called ‘genomatnn’ that jointly models introgression, which is the transfer of genetic information between species, and natural selection. The model was developed in order to identify regions in the human genome where this introgression could have happened,” says Associate Professor Fernando Racimo, GLOBE Institute, corresponding author of the new study. More

A team of scientists headed by SANKEN (The Institute of Scientific and Industrial Research) at Osaka University demonstrated that single virus particles passing through a nanopore could be accurately identified using machine learning. The test platform they created was so sensitive that the coronaviruses responsible for the common cold, SARS, MERS, and COVID could be distinguished from each other. This work may lead to rapid, portable, and accurate screening tests for COVID and other viral diseases.
The global coronavirus pandemic has revealed the crucial need for rapid pathogen screening. However, the current gold-standard for detecting RNA viruses — including SARS-CoV-2, the virus that causes COVID — is reverse transcription-polymerase chain reaction (RT-PCR) testing. While accurate, this method is relatively slow, which hinders the timely interventions required to control an outbreak.
Now, scientists led by Osaka University have developed an intelligent nanopore system that can be used for the detection of SARS-CoV-2 virus particles. Using machine-learning methods, the platform can accurately discriminate between similarly sized coronaviruses responsible for different respiratory diseases. “Our innovative technology has high sensitivity and can even electrically identify single virus particles,” first author Professor Masateru Taniguchi says. Using this platform, the researchers were able to achieve a sensitivity of 90% and a specificity of 96% for SARS-CoV-2 detection in just five minutes using clinical saliva samples.
To fabricate the device, nanopores just 300 nanometers in diameter were bored into a silicon nitride membrane. When a virus was pulled through a nanopore by the electrophoretic force, the opening became partially blocked. This temporarily decreased the ionic flow inside the nanopore, which was detected as a change in the electrical current. The current as a function of time provided information on the volume, structure, and surface charge of the target being analyzed. However, to interpret the subtle signals, which could be as small as a few nanoamps, machine learning was needed. The team used 40 PCR-positive and 40 PCR-negative saliva samples to train the algorithm.
“We expect that this research will enable rapid point-of-care and screening tests for SARS-CoV-2 without the need for RNA extraction,” Professor Masateru Taniguchi explains. “A user-friendly and non-invasive method such as this is more amenable to immediate diagnosis in hospitals and screening in places where large crowds are gathered.” The complete test platform consists of machine learning software on a server, a portable high-precision current measuring instrument, and cost-effective semiconducting nanopore modules. By using a machine-learning method, the researchers expect that this system can be adapted for use in the detection of emerging infectious diseases in the future. The team hopes that this approach will revolutionize public health and disease control.
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Electrons are ubiquitous among atoms, subatomic tokens of energy that can independently change how a system behaves — but they also can change each other. An international research collaboration found that collectively measuring electrons revealed unique and unanticipated findings. The researchers published their results on May 17 in Physical Review Letters.
“It is not feasible to obtain the solution just by tracing the behavior of each individual electron,” said paper author Myung Joon Han, professor of physics at KAIST. “Instead, one should describe or track all the entangled electrons at once. This requires a clever way of treating this entanglement.”
Professor Han and the researchers used a recently developed “many-particle” theory to account for the entangled nature of electrons in solids, which approximates how electrons locally interact with one another to predict their global activity.
Through this approach, the researchers examined systems with two orbitals — the space in which electrons can inhabit. They found that the electrons locked into parallel arrangements within atom sites in solids. This phenomenon, known as Hund’s coupling, results in a Hund’s metal. This metallic phase, which can give rise to such properties as superconductivity, was thought only to exist in three-orbital systems.
“Our finding overturns a conventional viewpoint that at least three orbitals are needed for Hund’s metallicity to emerge,” Professor Han said, noting that two-orbital systems have not been a focus of attention for many physicists. “In addition to this finding of a Hund’s metal, we identified various metallic regimes that can naturally occur in generic, correlated electron materials.”
The researchers found four different correlated metals. One stems from the proximity to a Mott insulator, a state of a solid material that should be conductive but actually prevents conduction due to how the electrons interact. The other three metals form as electrons align their magnetic moments — or phases of producing a magnetic field — at various distances from the Mott insulator. Beyond identifying the metal phases, the researchers also suggested classification criteria to define each metal phase in other systems.
“This research will help scientists better characterize and understand the deeper nature of so-called ‘strongly correlated materials,’ in which the standard theory of solids breaks down due to the presence of strong Coulomb interactions between electrons,” Professor Han said, referring to the force with which the electrons attract or repel each other. These interactions are not typically present in solid materials but appear in materials with metallic phases.
The revelation of metals in two-orbital systems and the ability to determine whole system electron behavior could lead to even more discoveries, according to Professor Han.
“This will ultimately enable us to manipulate and control a variety of electron correlation phenomena,” Professor Han said.
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Materials provided by The Korea Advanced Institute of Science and Technology (KAIST). Note: Content may be edited for style and length. More

AI is used in an array of extremely useful applications, such as predicting a machine’s lifetime through its vibrations, monitoring the cardiac activity of patients and incorporating facial recognition capabilities into video surveillance systems. The downside is that AI-based technology generally requires a lot of power and, in most cases, must be permanently connected to the cloud, raising issues related to data protection, IT security and energy use.
CSEM engineers may have found a way to get around those issues, thanks to a new system-on-chip they have developed. It runs on a tiny battery or a small solar cell and executes AI operations at the edge — i.e., locally on the chip rather than in the cloud. What’s more, their system is fully modular and can be tailored to any application where real-time signal and image processing is required, especially when sensitive data are involved. The engineers will present their device at the prestigious 2021 VLSI Circuits Symposium in Kyoto this June.
The CSEM system-on-chip works through an entirely new signal processing architecture that minimizes the amount of power needed. It consists of an ASIC chip with a RISC-V processor (also developed at CSEM) and two tightly coupled machine-learning accelerators: one for face detection, for example, and one for classification. The first is a binary decision tree (BDT) engine that can perform simple tasks but cannot carry out recognition operations.
“When our system is used in facial recognition applications, for example, the first accelerator will answer preliminary questions like: Are there people in the images? And if so, are their faces visible?” says Stéphane Emery, head of system-on-chip research at CSEM. “If our system is used in voice recognition, the first accelerator will determine whether noise is present and if that noise corresponds to human voices. But it can’t make out specific voices or words — that’s where the second accelerator comes in.”
The second accelerator is a convolutional neural network (CNN) engine that can perform these more complicated tasks — recognizing individual faces and detecting specific words — but it also consumes more energy. This two-tiered data processing approach drastically reduces the system’s power requirement, since most of the time only the first accelerator is running.
As part of their research, the engineers enhanced the performance of the accelerators themselves, making them adaptable to any application where time-based signal and image processing is needed. “Our system works in basically the same way regardless of the application,” says Emery. “We just have to reconfigure the various layers of our CNN engine.”
The CSEM innovation opens the door to an entirely new generation of devices with processors that can run independently for over a year. It also sharply reduces the installation and maintenance costs for such devices, and enables them to be used in places where it would be hard to change the battery.
Video: https://www.youtube.com/watch?v=2wJi4BHdXGo&t=2s
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As almost all our private information is digitalized, it is increasingly important that we find ways to protect our data and ourselves from being hacked.
Quantum Cryptography is the researchers’ answer to this problem, and more specifically a certain kind of qubit — consisting of single photons: particles of light.
Single photons or qubits of light, as they are also called, are extremely difficult to hack.
However, in order for these qubits of light to be stable and work properly they need to be stored at temperatures close to absolute zero — that is minus 270 C — something that requires huge amounts of power and resources.
Yet in a recently published study, researchers from University of Copenhagen, demonstrate a new way to store these qubits at room temperature for a hundred times longer than ever shown before.
“We have developed a special coating for our memory chips that helps the quantum bits of light to be identical and stable while being in room temperature. In addition, our new method enables us to store the qubits for a much longer time, which is milliseconds instead of microseconds — something that has not been possible before. We are really excited about it,” says Eugene Simon Polzik, professor in quantum optics at the Niels Bohr Institute. More

An international team of physicists led by the University of Minnesota has discovered that a unique superconducting metal is more resilient when used as a very thin layer. The research is the first step toward a larger goal of understanding unconventional superconducting states in materials, which could possibly be used in quantum computing in the future.
The collaboration includes four faculty members in the University of Minnesota’s School of Physics and Astronomy — Associate Professor Vlad Pribiag, Professor Rafael Fernandes, and Assistant Professors Fiona Burnell and Ke Wang — along with physicists at Cornell University and several other institutions. The study is published in Nature Physics, a monthly, peer-reviewed scientific journal published by the Nature Research.
Niobium diselenide (NbSe2) is a superconducting metal, meaning that it can conduct electricity, or transport electrons from one atom to another, with no resistance. It is not uncommon for materials to behave differently when they are at a very small size, but NbSe2 has potentially beneficial properties. The researchers found that the material in 2D form (a very thin substrate only a few atomic layers thick) is a more resilient superconductor because it has a two-fold symmetry, which is very different from thicker samples of the same material.
Motivated by Fernandes and Burnell’s theoretical prediction of exotic superconductivity in this 2D material, Pribiag and Wang started to investigate atomically-thin 2D superconducting devices.
“We expected it to have a six-fold rotational pattern, like a snowflake.” Wang said. “Despite the six-fold structure, it only showed two-fold behavior in the experiment.”
“This was one of the first times [this phenomenon] was seen in a real material,” Pribiag said.
The researchers attributed the newly-discovered two-fold rotational symmetry of the superconducting state in NbSe2 to the mixing between two closely competing types of superconductivity, namely the conventional s-wave type — typical of bulk NbSe2 — and an unconventional d- or p-type mechanism that emerges in few-layer NbSe2. The two types of superconductivity have very similar energies in this system. Because of this, they interact and compete with each other.
Pribiag and Wang said they later became aware that physicists at Cornell University were reviewing the same physics using a different experimental technique, namely quantum tunneling measurements. They decided to combine their results with the Cornell research and publish a comprehensive study.
Burnell, Pribiag, and Wang plan to build on these initial results to further investigate the properties of atomically thin NbSe2 in combination with other exotic 2D materials, which could ultimately lead to the use of unconventional superconducting states, such as topological superconductivity, to build quantum computers.
“What we want is a completely flat interface on the atomic scale,” Pribiag said. “We believe this system will be able to give us a better platform to study materials to use them for quantum computing applications.”
In addition to Pribiag, Fernandes, Burnell, Wang, the collaboration included University of Minnesota physics graduate students Alex Hamill, Brett Heischmidt, Daniel Shaffer, Kan-Ting Tsai, and Xi Zhang; Cornell University faculty members Jie Shan and Kin Fai Mak and graduate student Egon Sohn; Helmuth Berger and László Forró, researchers at Ecole Polytechnique Fédérale de Lausanne in Switzerland; Alexey Suslov, a researcher at the National High Magnetic Field Laboratory in Tallahassee, Fla.; and Xiaoxiang Xi, a professor at Nanjing University in China.
The University of Minnesota research was supported primarily by the National Science Foundation (NSF) through the University of Minnesota Materials Research Science and Engineering Center (MRSEC). The research at Cornell was supported by the Office of Naval Research (ONR) and NSF. The work in Switzerland was supported by the Swiss National Science Foundation.
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Do you like the thick brush strokes and soft color palettes of an impressionist painting such as those by Claude Monet? Or do you prefer the bold colors and abstract shapes of a Rothko? Individual art tastes have a certain mystique to them, but now a new Caltech study shows that a simple computer program can accurately predict which paintings a person will like.
The new study, appearing in the journal Nature Human Behaviour, utilized Amazon’s crowdsourcing platform Mechanical Turk to enlist more than 1,500 volunteers to rate paintings in the genres of impressionism, cubism, abstract, and color field. The volunteers’ answers were fed into a computer program and then, after this training period, the computer could predict the volunteers’ art preferences much better than would happen by chance.
“I used to think the evaluation of art was personal and subjective, so I was surprised by this result,” says lead author Kiyohito Iigaya, a postdoctoral scholar who works in the laboratory of Caltech professor of psychology John O’Doherty.
The findings not only demonstrated that computers can make these predictions but also led to a new understanding about how people judge art.
“The main point is that we are gaining an insight into the mechanism that people use to make aesthetic judgments,” says O’Doherty. “That is, that people appear to use elementary image features and combine over them. That’s a first step to understanding how the process works.”
In the study, the team programmed the computer to break a painting’s visual attributes down into what they called low-level features — traits like contrast, saturation, and hue — as well as high-level features, which require human judgment and include traits such as whether the painting is dynamic or still. More