Computers Math

A roadmap for the future direction of quantum simulation has been set out in a paper co-authored at the University of Strathclyde.
Quantum computers are hugely powerful devices with a capacity for speed and calculation which is well beyond the reach of classical, or binary, computing. Instead of a binary system of zeroes and ones, it operates through superpositions, which may be zeroes, ones or both at the same time.
The continuously-evolving development of quantum computing has reached the point of having an advantage over classical computers for an artificial problem. It could have future applications in a wide range of areas. One promising class of problems involves the simulation of quantum systems, with potential applications such as developing materials for batteries, industrial catalysis and nitrogen fixing.
The paper, published in Nature, explores near- and medium-term possibilities for quantum simulation on analogue and digital platforms to help evaluate the potential of this area. It has been co-written by researchers from Strathclyde, the Max Planck Institute of Quantum Optics, Ludwig Maximilians University in Munich, Munich Center for Quantum Science and Technology, the University of Innsbruck, the Institute for Quantum Optics and Quantum Information of the Austrian Academy of Sciences, and Microsoft Corporation.
Professor Andrew Daley, of Strathclyde’s Department of Physics, is lead author of the paper. He said: “There has been a great deal of exciting progress in analogue and digital quantum simulation in recent years, and quantum simulation is one of the most promising fields of quantum information processing. It is already quite mature, both in terms of algorithm development, and in the availability of significantly advanced analogue quantum simulation experiments internationally.
“In computing history, classical analogue and digital computing co-existed for more than half a century, with a gradual transition towards digital computing, and we expect the same thing to happen with the emergence of quantum simulation. More

At the beginning of the COVID-19 pandemic, car manufacturing companies such as Ford quickly shifted their production focus from automobiles to masks and ventilators.
To make this switch possible, these companies relied on people working on an assembly line. It would have been too challenging for a robot to make this transition because robots are tied to their usual tasks.
Theoretically, a robot could pick up almost anything if its grippers could be swapped out for each task. To keep costs down, these grippers could be passive, meaning grippers pick up objects without changing shape, similar to how the tongs on a forklift work.
A University of Washington team created a new tool that can design a 3D-printable passive gripper and calculate the best path to pick up an object. The team tested this system on a suite of 22 objects — including a 3D-printed bunny, a doorstop-shaped wedge, a tennis ball and a drill. The designed grippers and paths were successful for 20 of the objects. Two of these were the wedge and a pyramid shape with a curved keyhole. Both shapes are challenging for multiple types of grippers to pick up.
The team will present these findings Aug. 11 at SIGGRAPH 2022.
“We still produce most of our items with assembly lines, which are really great but also very rigid. The pandemic showed us that we need to have a way to easily repurpose these production lines,” said senior author Adriana Schulz, a UW assistant professor in the Paul G. Allen School of Computer Science & Engineering. “Our idea is to create custom tooling for these manufacturing lines. That gives us a very simple robot that can do one task with a specific gripper. And then when I change the task, I just replace the gripper.”
Passive grippers can’t adjust to fit the object they’re picking up, so traditionally, objects have been designed to match a specific gripper. More

Researchers from Aarhus and Berlin have developed a new algorithm that can teach computers to predict how complex molecules will bind to the surface of catalysts. This is important when you have to produce synthetic fuels, for example. And it’s almost like playing extreme Tetris.
Imagine a game of Tetris where you not only have to stack the pieces in three dimensions, but the pieces are also much more complicated than the seven geometric shapes you normally use in the game.
In this case, the pieces are large and complex molecules that are to bind to another material in a chemical reaction.
To make things even harder, both the molecules and the other material have several places on the surface where they can bind to each other — and it is crucial that the binding is neither too weak nor too strong.
The binding has to be exactly right, otherwise the other material cannot function as a catalyst (see fact box at the end of the text).
Such an extreme game of Tetris perfectly illustrates the challenges that researchers all over the world encounter when working on developing new and better catalysts for a wide range of technical-chemical processes. More

Ultrasound imaging is a safe and noninvasive window into the body’s workings, providing clinicians with live images of a patient’s internal organs. To capture these images, trained technicians manipulate ultrasound wands and probes to direct sound waves into the body. These waves reflect back out to produce high-resolution images of a patient’s heart, lungs, and other deep organs.
Currently, ultrasound imaging requires bulky and specialized equipment available only in hospitals and doctor’s offices. But a new design by MIT engineers might make the technology as wearable and accessible as buying Band-Aids at the pharmacy.
In a paper appearing today in Science, the engineers present the design for a new ultrasound sticker — a stamp-sized device that sticks to skin and can provide continuous ultrasound imaging of internal organs for 48 hours.
The researchers applied the stickers to volunteers and showed the devices produced live, high-resolution images of major blood vessels and deeper organs such as the heart, lungs, and stomach. The stickers maintained a strong adhesion and captured changes in underlying organs as volunteers performed various activities, including sitting, standing, jogging, and biking.
The current design requires connecting the stickers to instruments that translate the reflected sound waves into images. The researchers point out that even in their current form, the stickers could have immediate applications: For instance, the devices could be applied to patients in the hospital, similar to heart-monitoring EKG stickers, and could continuously image internal organs without requiring a technician to hold a probe in place for long periods of time.
If the devices can be made to operate wirelessly — a goal the team is currently working toward — the ultrasound stickers could be made into wearable imaging products that patients could take home from a doctor’s office or even buy at a pharmacy. More

As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.
Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.
A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.
Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.
“With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”
“The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering. More

What are the chances you will contract Covid-19 on a plane flight? A study led by MIT scholars offers a calculation of that for the period from June 2020 through February 2021. While the conditions that applied at that stage of the Covid-19 pandemic differ from those of today, the study offers a method that could be adapted as the pandemic evolves.
The study estimates that from mid-2020 through early 2021, the probability of getting Covid-19 on an airplane surpassed 1 in 1,000 on a totally full flight lasting two hours at the height of the early pandemic, roughly December 2020 and January 2021. It dropped to about 1 in 6,000 on a half-full two-hour flight when the pandemic was at its least severe, in the summer of 2020. The overall risk of transmission from June 2020 through February 2021 was about 1 in 2,000, with a mean of 1 in 1,400 and a median of 1 in 2,250.
To be clear, current conditions differ from the study’s setting. Masks are no longer required for U.S. domestic passengers; in the study’s time period, airlines were commonly leaving middle seats open, which they are no longer doing; and newer Covid-19 variants are more contagious than the virus was during the study period. While those factors may increase the current risk, most people have received Covid-19 vaccinations since February 2021, which could serve to lower today’s risk — though the precise impact of those vaccines against new variants is uncertain.
Still, the study does provide a general estimate about air travel safety with regard to Covid-19 transmission, and a methodology that can be applied to future studies. Some U.S. carriers at the time stated that onboard transmission was “virtually nonexistent” and “nearly nonexistent,” but as the research shows, there was a discernible risk. On the other hand, passengers were not exactly facing coin-flip odds of catching the virus in flight, either.
“The aim is to set out the facts,” says Arnold Barnett, a management professor at MIT and aviation risk expert, who is co-author of a recent paper detailing the study’s results. “Some people might say, ‘Oh, that doesn’t sound like very much.’ But if we at least tell people what the risk is, they can make judgments.”
As Barnett also observes, a round-trip flight with a change of planes and two two-hour segments in each direction counts as four flights in this accounting, so a 1 in 1,000 probability, per flight, would lead to approximately a 1 in 250 chance for such a trip as a whole. More

Constructing a tiny robot from DNA and using it to study cell processes invisible to the naked eye… You would be forgiven for thinking it is science fiction, but it is in fact the subject of serious research by scientists from Inserm, CNRS and Université de Montpellier at the Structural Biology Center in Montpellier[1]. This highly innovative “nano-robot” should enable closer study of the mechanical forces applied at microscopic levels, which are crucial for many biological and pathological processes. It is described in a new study published in Nature Communications.
Our cells are subject to mechanical forces exerted on a microscopic scale, triggering biological signals essential to many cell processes involved in the normal functioning of our body or in the development of diseases.
For example, the feeling of touch is partly conditional on the application of mechanical forces on specific cell receptors (the discovery of which was this year rewarded by the Nobel Prize in Physiology or Medicine). In addition to touch, these receptors that are sensitive to mechanical forces (known as mechanoreceptors) enable the regulation of other key biological processes such as blood vessel constriction, pain perception, breathing or even the detection of sound waves in the ear, etc.
The dysfunction of this cellular mechanosensitivity is involved in many diseases — for example, cancer: cancer cells migrate within the body by sounding and constantly adapting to the mechanical properties of their microenvironment. Such adaptation is only possible because specific forces are detected by mechanoreceptors that transmit the information to the cell cytoskeleton.
At present, our knowledge of these molecular mechanisms involved in cell mechanosensitivity is still very limited. Several technologies are already available to apply controlled forces and study these mechanisms, but they have a number of limitations. In particular, they are very costly and do not allow us to study several cell receptors at a time, which makes their use very time-consuming if we want to collect a lot of data.
DNA origami structures
In order to propose an alternative, the research team led by Inserm researcher Gaëtan Bellot at the Structural Biology Center (Inserm/CNRS/Université de Montpellier) decided to use the DNA origami method. This enables the self-assembly of 3D nanostructures in a pre-defined form using the DNA molecule as construction material. Over the last ten years, the technique has allowed major advances in the field of nanotechnology.
This enabled the researchers to design a “nano-robot” composed of three DNA origami structures. Of nanometric size, it is therefore compatible with the size of a human cell. It makes it possible for the first time to apply and control a force with a resolution of 1 piconewton, namely one trillionth of a Newton — with 1 Newton corresponding to the force of a finger clicking on a pen. This is the first time that a human-made, self-assembled DNA-based object can apply force with this accuracy.
The team began by coupling the robot with a molecule that recognizes a mechanoreceptor. This made it possible to direct the robot to some of our cells and specifically apply forces to targeted mechanoreceptors localized on the surface of the cells in order to activate them.
Such a tool is very valuable for basic research, as it could be used to better understand the molecular mechanisms involved in cell mechanosensitivity and discover new cell receptors sensitive to mechanical forces. Thanks to the robot, the scientists will also be able to study more precisely at what moment, when applying force, key signaling pathways for many biological and pathological processes are activated at cell level.
“The design of a robot enabling the in vitro and in vivo application of piconewton forces meets a growing demand in the scientific community and represents a major technological advance. However, the biocompatibility of the robot can be considered both an advantage for in vivo applications but may also represent a weakness with sensitivity to enzymes that can degrade DNA. So our next step will be to study how we can modify the surface of the robot so that it is less sensitive to the action of enzymes. We will also try to find other modes of activation of our robot using, for example, a magnetic field,” emphasizes Bellot.
[1] Also contributed to this research: the Institute of Functional Genomics (CNRS/Inserm/Université de Montpellier), the Max Mousseron Biomolecules Institute (CNRS/Université de Montpellier/ENSCM), the Paul Pascal Research Center (CNRS/Université de Bordeaux) and the Physiology and Experimental Medicine: Heart-Muscles laboratory (CNRS/Inserm/Université de Montpellier). More

A Nagoya University research group has developed an AI algorithm that accurately and quickly diagnoses idiopathic pulmonary fibrosis, a lung disease. The algorithm makes its diagnosis based only on information from non-invasive examinations, including lung images and medical information collected during daily medical care.
Doctors have waited a long time for an early means of diagnosing idiopathic pulmonary fibrosis, a potentially fatal disease that can scar a person’s lungs. Except for drugs that can delay the disease’s progression, established therapies do not exist. Since doctors face many difficulties diagnosing the disease, they often have to request a specialist diagnosis. In addition, many of the diagnostic techniques, such as lung biopsy, are highly invasive. These investigative measures may exacerbate the disease, increasing a patient’s risk of dying.
Taiki Furukawa, Assistant Professor of the Nagoya University Hospital, in collaboration with RIKEN and Tosei General Hospital, has developed a new technology to diagnose idiopathic pulmonary fibrosis. Using artificial intelligence (AI), the group analyzed medical data from patients in Tosei General Hospital’s interstitial pneumonia treatment facility, collected during normal care. They found that their AI diagnosed idiopathic pulmonary fibrosis with a similar level of accuracy as a human specialist. They published their results in the journal Respirology.
Despite finding that their AI performed just as well as experts, the team stress that they do not see it as replacing medical professionals. Instead, they hope that specialists will use AI in medical treatment to ensure that they do not miss opportunities for early treatment. Its use would also avoid invasive procedures, such as lung biopsies, which could save lives.
“Idiopathic pulmonary fibrosis has a very poor prognosis among lung diseases,” Furukawa says. “It has been difficult to diagnose even for general respiratory physicians. The diagnostic AI developed in this study would allow any hospital to get a diagnosis equivalent to that of a specialist. For idiopathic pulmonary fibrosis, the developed diagnostic AI is useful as a screening tool and may lead to personalized medicine by collaborating with medical specialists.”
Furukawa is excited about the possibilities: “The practical application of diagnostic AI and collaborative diagnosis with specialists may lead to a more accurate diagnosis and treatment. We expect it to revolutionize medical care.”
This study was supported by JSPS KAKENHI, Grant/Award Number: JP19110253; The Hori Science and Arts Foundation; The Japanese Respiratory Foundation.
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Materials provided by Nagoya University. Note: Content may be edited for style and length. More