Aurelia Butler

Researchers in Delft have succeeded in teleporting quantum information across a rudimentary network. This first of its kind is an important step towards a future quantum Internet. This breakthrough was made possible by a greatly improved quantum memory and enhanced quality of the quantum links between the three nodes of the network. The researchers, working at QuTech — a collaboration between Delft University of Technology and the Netherlands Organisation for Applied Scientific Research (TNO) — are publishing their findings today in the scientific journal Nature.
The power of a future quantum Internet is based on the ability to send quantum information (quantum bits) between the nodes of the network. This will enable all kinds of applications such as securely sharing confidential information, linking several quantum computers together to increase their computing capability, and the use of highly precise, linked quantum sensors.
Sending quantum information
The nodes of such a quantum network consist of small quantum processors. Sending quantum information between these processors is no easy feat. One possibility is to send quantum bits using light particles but, due to the inevitable losses in glass fibre cables, in particular over long distances, the light particles will very likely not reach their destination. As it is fundamentally impossible to simply copy quantum bits, the loss of a light particle means that the quantum information is irrecoverably lost.
Teleportation offers a better way of sending quantum information. The protocol for quantum teleportation owes its name to similarities with teleportation in science-fiction films: the quantum bit disappears on the side of the sender and appears on the side of the receiver. As the quantum bit therefore does not need to travel across the intervening space, there is no chance that it will be lost. This makes quantum teleportation an crucial technique for a future quantum Internet.
Good control over the system
In order to be able to teleport quantum bits, several ingredients are required: a quantum entangled link between the sender and receiver, a reliable method for reading out quantum processors, and the capacity to temporarily store quantum bits. Previous research at QuTech demonstrated that it is possible to teleport quantum bits between two adjacent nodes. The researchers at QuTech have now shown for the first time that they can meet the package of requirements and have demonstrated teleportation between non-adjacent nodes, in other words over a network. They teleported quantum bits from node “Charlie” to node “Alice,” with the help of an intermediate node “Bob.” More

Researchers from Carnegie Mellon University took an all-terrain vehicle on wild rides through tall grass, loose gravel and mud to gather data about how the ATV interacted with a challenging, off-road environment.
They drove the heavily instrumented ATV aggressively at speeds up to 30 miles an hour. They slid through turns, took it up and down hills, and even got it stuck in the mud — all while gathering data such as video, the speed of each wheel and the amount of suspension shock travel from seven types of sensors.
The resulting dataset, called TartanDrive, includes about 200,000 of these real-world interactions. The researchers believe the data is the largest real-world, multimodal, off-road driving dataset, both in terms of the number of interactions and types of sensors. The five hours of data could be useful for training a self-driving vehicle to navigate off road.
“Unlike autonomous street driving, off-road driving is more challenging because you have to understand the dynamics of the terrain in order to drive safely and to drive faster,” said Wenshan Wang, a project scientist in the Robotics Institute (RI).
Previous work on off-road driving has often involved annotated maps, which provide labels such as mud, grass, vegetation or water to help the robot understand the terrain. But that sort of information isn’t often available and, even when it is, might not be useful. A map area labeled as “mud,” for example, may or may not be drivable. Robots that understand dynamics can reason about the physical world.
The research team found that the multimodal sensor data they gathered for TartanDrive enabled them to build prediction models superior to those developed with simpler, nondynamic data. Driving aggressively also pushed the ATV into a performance realm where an understanding of dynamics became essential, said Samuel Triest, a second-year master’s student in robotics.
“The dynamics of these systems tend to get more challenging as you add more speed,” said Triest, who was lead author on the team’s resulting paper. “You drive faster, you bounce off more stuff. A lot of the data we were interested in gathering was this more aggressive driving, more challenging slopes and thicker vegetation because that’s where some of the simpler rules start breaking down.”
Though most work on self-driving vehicles focuses on street driving, the first applications likely will be off road in controlled access areas, where the risk of collisions with people or other vehicles is limited. The team’s tests were performed at a site near Pittsburgh that CMU’s National Robotics Engineering Center uses to test autonomous off-road vehicles. Humans drove the ATV, though they used a drive-by-wire system to control steering and speed.
“We were forcing the human to go through the same control interface as the robot would,” Wang said. “In that way, the actions the human takes can be used directly as input for how the robot should act.”
Triest will present the TartanDrive study at the International Conference on Robotics and Automation (ICRA) this week in Philadelphia. In addition to Triest and Wang, the research team included Sebastian Scherer, associate research professor in the RI; Aaron Johnson, an assistant professor of mechanical engineering; Sean J. Wang, a Ph.D. student in mechanical engineering; and Matthew Sivaprakasam, a computer engineering student at the University of Pittsburgh.
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Materials provided by Carnegie Mellon University. Original written by Byron Spice. Note: Content may be edited for style and length. More

For quantum computers to be useful in practice, errors must be detected and corrected. At the University of Innsbruck, Austria, a team of experimental physicists has now implemented a universal set of computational operations on fault-tolerant quantum bits for the first time, demonstrating how an algorithm can be programmed on a quantum computer so that errors do not spoil the result.
In modern computers errors during processing and storage of information have become a rarity due to high-quality fabrication. However, for critical applications, where even single errors can have serious effects, error correction mechanisms based on redundancy of the processed data are still used.
Quantum computers are inherently much more susceptible to disturbances and will thus probably always require error correction mechanisms, because otherwise errors will propagate uncontrolled in the system and information will be lost. Because the fundamental laws of quantum mechanics forbid copying quantum information, redundancy can be achieved by distributing logical quantum information into an entangled state of several physical systems, for example multiple individual atoms.
The team led by Thomas Monz of the Department of Experimental Physics at the University of Innsbruck and Markus Müller of RWTH Aachen University and Forschungszentrum Jülich in Germany has now succeeded for the first time in realizing a set of computational operations on two logical quantum bits that can be used to implement any possible operation. “For a real-world quantum computer, we need a universal set of gates with which we can program all algorithms,” explains Lukas Postler, an experimental physicist from Innsbruck.
Fundamental quantum operation realized
The team of researchers implemented this universal gate set on an ion trap quantum computer featuring 16 trapped atoms. The quantum information was stored in two logical quantum bits, each distributed over seven atoms. More

While quantum computers offer many novel possibilities, they also pose a threat to internet security since these supercomputers make common encryption methods vulnerable. Based on the so-called quantum key distribution, researchers at TU Darmstadt have developed a new, tap-proof communication network.
The new system is used to exchange symmetric keys between parties in order to encrypt messages so that they cannot be read by third parties. In cooperation with Deutsche Telekom, the researchers led by physics professor Thomas Walther succeeded in operating a quantum network that is scalable in terms of the number of users and at the same time robust without the need for trusted nodes. In the future, such systems could protect critical infrastructure from the growing danger of cyberattacks. In addition, tap-proof connections could be installed between different government sites in larger cities.
The system developed by the Darmstadt researchers enables the so-called quantum key exchange, providing several parties in a star-shaped network with a common random number. Individual light quanta, so-called photons, are distributed to users in the communication network in order to calculate the random number and thus the digital key. Due to quantum physical effects, these keys are particularly secure. In this way, communication is particularly highly protected, and existing eavesdropping attacks can be detected.
So far, such quantum key methods have been technically complex and sensitive to external influences. The system of the Darmstadt group from the Collaborative Research Center CROSSING is based on a special protocol. The system distributes photons from a central source to all users in the network and establishes the security of the quantum keys through the effect of so-called quantum entanglement. This quantum-physical effect produces correlations between two light particles, which are observable even when they are far apart. The property of the partner particle can be predicted by measuring a property of the light particle from a pair.
Polarization is often used as a property, but this is typically disturbed in the glass fibers used for transmission due to environmental influences such as vibrations or small temperature changes. However, the Darmstadt system uses a protocol in which the quantum information is encoded in the phase and arrival time of the photons and is therefore particularly insensitive to such disturbances. For the first time, the group has succeeded in providing a network of users with quantum keys by means of this robust protocol.
The high stability of the transmission and the scalability in principle were successfully demonstrated in a field test together with Deutsche Telekom Technik GmbH. As a next step, the researchers at TU Darmstadt are planning to connect other buildings in the city to their system.
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Materials provided by Technische Universitat Darmstadt. Note: Content may be edited for style and length. More

An artificial intelligence (AI) tool helps doctors predict the cancer risk in lung nodules seen on CT, according to a new study published in the journal Radiology.
Pulmonary nodules appear as small spots on the lungs on chest imaging. They have become a much more common finding as CT has gained favor over X-rays for chest imaging.
“A nodule would appear on somewhere between 5% to 8% of chest X-rays,” said study senior author Anil Vachani, M.D., director of clinical research in the section of Interventional Pulmonology and Thoracic Oncology at the Perelman School of Medicine, University of Pennsylvania in Philadelphia. “Chest CT is such a sensitive test, you’ll see a small nodule in upwards of a third to a half of cases. We’ve gone from a problem that was relatively uncommon to one that affects 1.6 million people in the U.S. every year.”
Dr. Vachani and colleagues evaluated an AI-based computer-aided diagnosis tool developed by Optellum Ltd. of Oxford, England, to assist clinicians in assessing pulmonary nodules on chest CT. While CT scans show many aspects of the nodule, such as size and border characteristics, AI can delve even deeper.
“AI can go through very large datasets to come up with unique patterns that can’t be seen through the naked eye and end up being predictive of malignancy,” Dr. Vachani said.
In the study, six radiologists and six pulmonologists made estimates of malignancy risk for nodules using CT imaging data alone. They also made management recommendations such as CT surveillance or a diagnostic procedure for each case without and with the AI tool.
A total of 300 chest CTs of indeterminant pulmonary nodules were used in the study. The researchers defined indeterminant nodules as those between 5 and 30 millimeters in diameter.
Analysis showed that use of the AI tool improved estimation of nodule malignancy risk on chest CT. It also improved agreement among the different readers for both risk stratification and management recommendations.
“The readers judge malignant or benign with a reasonable level of accuracy based on imaging itself, but when you combine their clinical interpretation with the AI algorithm, the accuracy level improves significantly,” Dr. Vachani said. “The level of improvement suggests that this tool has the potential to change how we judge cancer versus benign and hopefully improve how we manage patients.”
The model appears to work equally well on diagnostic CT and low-dose screening CT, Dr. Vachani said, but more study is needed before the AI tool can be used in the clinic.
“We’ve taken the first step here and shown that decision making is better if the AI tool is incorporated into radiology or pulmonology practice,” Dr. Vachani said. “The next step is to take the tool and do some prospective trials where physicians use the AI tool in a real-world setting. We are in the process of designing those trials.”
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Materials provided by Radiological Society of North America. Note: Content may be edited for style and length. More

Artificial intelligence (AI) algorithms trained on real astronomical observations now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies and detect the mergers of massive stars, accelerating the rate of new discovery in the world’s oldest science.
But AI, also called machine learning, can reveal something deeper, University of California, Berkeley, astronomers found: unsuspected connections hidden in the complex mathematics arising from general relativity — in particular, how that theory is applied to finding new planets around other stars.
In a paper appearing this week in the journal Nature Astronomy, the researchers describe how an AI algorithm developed to more quickly detect exoplanets when such planetary systems pass in front of a background star and briefly brighten it — a process called gravitational microlensing — revealed that the decades-old theories now used to explain these observations are woefully incomplete.
In 1936, Albert Einstein himself used his new theory of general relativity to show how the light from a distant star can be bent by the gravity of a foreground star, not only brightening it as seen from Earth, but often splitting it into several points of light or distorting it into a ring, now called an Einstein ring. This is similar to the way a hand lens can focus and intensify light from the sun.
But when the foreground object is a star with a planet, the brightening over time — the light curve — is more complicated. What’s more, there are often multiple planetary orbits that can explain a given light curve equally well — so called degeneracies. That’s where humans simplified the math and missed the bigger picture.
The AI algorithm, however, pointed to a mathematical way to unify the two major kinds of degeneracy in interpreting what telescopes detect during microlensing, showing that the two “theories” are really special cases of a broader theory that, the researchers admit, is likely still incomplete. More

High-fidelity quantum logic gates applied to quantum bits (qubits) are the basic building blocks of programmable quantum circuits. Researchers at the Advanced Quantum Testbed (AQT) at Lawrence Berkeley National Laboratory (Berkeley Lab) conducted the first experimental demonstration of a three-qubit high-fidelity iToffoli native gate in a superconducting quantum information processor and in a single step.
Noisy intermediate-scale quantum processors typically support one- or two-qubit native gates, the types of gates that can be implemented directly by hardware. More complex gates are implemented by breaking them up into sequences of native gates. The team’s demonstration adds a novel and robust native three-qubit iToffoli gate for universal quantum computing. Furthermore, the team demonstrated a very high fidelity operation of the gate at 98.26%. The team’s experimental breakthrough was published in Nature Physics this May.
Quantum Logic Gates, Quantum Circuits
The Toffoli or the controlled-controlled-NOT (CCNOT) is a key logical gate in classical computing because it is universal, so it can build all logic circuits to compute any desired binary operation. Furthermore, it is reversible, which allows the determination and recovery of the binary inputs (bits) from the outputs, so no information is lost.
In quantum circuits, the input qubit can be in a superposition of 0 and 1 states. The qubit is physically connected to other qubits in the circuit, which makes it more difficult to implement a high-fidelity quantum gate as the number of qubits increases. The fewer quantum gates needed to compute an operation, the shorter the quantum circuit, thereby improving the implementation of an algorithm before the qubits decohere causing errors in the final result. Therefore, reducing the complexity and running time of quantum gates is critical.
In tandem with the Hadamard gate, the Toffoli gate forms a universal quantum gate set, which allows researchers to run any quantum algorithm. Experiments implementing multi-qubit gates in major computing technologies — superconducting circuits, trapped ions, and Rydberg atoms — successfully demonstrated Toffoli gates on three-qubit gates with fidelities averaging between 87% and 90%. However, such demonstrations required researchers to break up the Toffoli gates into one- and two-qubit gates, making the gate operation time longer and degrading their fidelity. More

Scientists at the Broad Institute of MIT and Harvard and the University of Massachusetts Medical School have developed a machine learning model that can analyze millions of SARS-CoV-2 genomes and predict which viral variants will likely dominate and cause surges in COVID-19 cases. The model, called PyR0 (pronounced “pie-are-nought”), could help researchers identify which parts of the viral genome will be less likely to mutate and hence be good targets for vaccines that will work against future variants. The findings appear today in Science.
The researchers trained the machine-learning model using 6 million SARS-CoV-2 genomes that were in the GISAID database in January 2022. They showed how their tool can also estimate the effect of genetic mutations on the virus’s fitness — its ability to multiply and spread through a population. When the team tested their model on viral genomic data from January 2022, it predicted the rise of the BA.2 variant, which became dominant in many countries in March 2022. PyR0 would have also identified the alpha variant (B.1.1.7) by late November 2020, a month before the World Health Organization listed it as a variant of concern.
The research team includes first author Fritz Obermeyer, a machine learning fellow at the Broad Institute when the study began, and senior authors Jacob Lemieux, an instructor of medicine at Harvard Medical School and Massachusetts General Hospital, and Pardis Sabeti, an institute member at Broad, a professor at the Center for Systems Biology and the Department of Organismic and Evolutionary Biology at Harvard University, and a professor in the Department of Immunology and Infectious Disease at the Harvard T. H. Chan School of Public Health. Sabeti is also a Howard Hughes Medical Institute investigator.
PyR0 is based on a machine learning framework called Pyro, which was originally developed by a team at Uber AI Labs. In 2020, three members of that team including Obermeyer and Martin Jankowiak, the study’s second author, joined the Broad Institute and began applying the framework to biology.
“This work was the result of biologists and geneticists coming together with software engineers and computer scientists,” Lemieux said. “We were able to tackle some really challenging questions in public health that no single disciplinary approach could have answered on its own.”
“This kind of machine learning-based approach that looks at all the data and combines that into a single prediction is extremely valuable,” said Sabeti. “It gives us a leg up on identifying what’s emerging and could be a potential threat.”
The future of SARS-CoV-2 More