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Category: information science

New approach allows drone swarms to autonomously navigate complex environments at high speed

Unmanned aerial vehicles (UAVs), commonly known as drones, are now widely used worldwide to tackle various real-world tasks, including filming videos for various purposes, monitoring crops or other environments from above, assessing disaster zones, and conducting military operations. Despite their widespread use, most existing drones either need to be fully or partly operated by human agents.

In addition, many drones are unable to navigate cluttered, crowded or unknown environments without colliding with nearby objects. Those that can navigate these environments typically rely on expensive or bulky components, such as advanced sensors, graphics processing units (GPUs) or .

Researchers at Shanghai Jiao Tong University have recently introduced a new insect-inspired approach that could enable teams of multiple drones to autonomously navigate complex environments while moving at high speed. Their proposed approach, introduced in a paper published in Nature Machine Intelligence, relies on both a deep learning algorithm and core physics principles.

New tool predicts cardiovascular disease risk more accurately

A new risk prediction tool developed by the American Heart Association (AHA) estimated cardiovascular disease (CVD) risk in a diverse patient cohort more accurately than current models, according to a recent study published in Nature Medicine.

The tool, called the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations which was developed in 2023, could help health care providers more accurately identify patients who have higher CVD risk and enhance preventive care efforts, according to Sadiya Khan, the Magerstadt Professor of Cardiovascular Epidemiology and co-first author of the study.

“Evaluating the new PREVENT equations in a diverse sample of patients is critical to provide primary care providers and cardiologists with further assurance that they can utilize these equations to accurately predict patients’ CVD risk, particularly in vulnerable populations,” said Khan, who is also an associate professor of Medical Social Sciences in the Division of Determinants of Health and of Preventive Medicine in the Division of Epidemiology.

Hunting for quantum-classical crossover in condensed matter problems

The intensive pursuit for quantum advantage in terms of computational complexity has further led to a modernized crucial question of when and how will quantum computers outperform classical computers. The next milestone is undoubtedly the realization of quantum acceleration in practical problems. Here we provide a clear evidence and arguments that the primary target is likely to be condensed matter physics. Our primary contributions are summarized as follows: 1) Proposal of systematic error/runtime analysis on state-of-the-art classical algorithm based on tensor networks; 2) Dedicated and high-resolution analysis on quantum resource performed at the level of executable logical instructions; 3) Clarification of quantum-classical crosspoint for ground-state simulation to be within runtime of hours using only a few hundreds of thousand physical qubits for 2d Heisenberg and 2d Fermi-Hubbard models, assuming that logical qubits are encoded via the surface code with the physical error rate of p = 10–3. To our knowledge, we argue that condensed matter problems offer the earliest platform for demonstration of practical quantum advantage that is order-of-magnitude more feasible than ever known candidates, in terms of both qubit counts and total runtime.

Yoshioka, N., Okubo, T., Suzuki, Y. et al. Hunting for quantum-classical crossover in condensed matter problems. npj Quantum Inf 10, 45 (2024). https://doi.org/10.1038/s41534-024-00839-4

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Human-AI teamwork uncovers hidden magnetic states in quantum spin liquids

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don’t have much data. Conversely, successful machine learning (ML) tends to rely on large, high-quality data sets for training. So how can researchers harness AI effectively to support their investigations?

In Physical Review Research, scientists describe an approach for working with ML to tackle complex questions in condensed matter physics. Their method tackles hard problems which were previously unsolvable by physicist simulations or by ML algorithms alone.

The researchers were interested in frustrated magnets— in which competing interactions lead to exotic magnetic properties. Studying these materials has helped to advance our understanding of quantum computing and shed light on . However, frustrated magnets are very difficult to simulate, because of the constraints arising from the way magnetic ions interact.

What is the Church-Turing Thesis?

Modern-day computers have proved to be quite powerful in what they can do. The rise of AI has made things we previously only imagined possible. And the rate at which computers are increasing their computational power certainly makes it seem like we will be able to do almost anything with them. But as we’ve seen before, there are fundamental limits to what computers can do regardless of the processors or algorithms they use. This naturally leads us to ask what computers are capable of doing at their best and what their limits are. Which requires formalizing various definitions in computing.

This is exactly what happened in the early 20th century. Logicians & mathematicians were trying to formalize the foundations of mathematics through logic. One famous challenge based on this was the Entscheidungsproblem posed by David Hilbert and Wilhelm Ackermann. The problem asked if there exists an algorithm that can verify whether any mathematical statement is true or false based on provided axioms. Such an algorithm could be used to verify if any mathematical system is internally consistent. Kurt Gödel proved in 1931 that this problem could not be answered one way or the other through his incompleteness theorems.

Years later, Alan Turing and Alonzo Church proved the same through separate, independent means. Turing did so by developing Turing machines (called automatic machines at the time) and the Halting problem. Church did so by developing lambda calculus. Later on, it was proved that Turing machines and lambda calculus are mathematically equivalent. This led many mathematicians to theorize that computability could be defined by either of these systems. That in turn caused Turing and Church to make their thesis: every effectively calculable function is a computable function. In simpler terms, it states that any computation from any model can be carried out by a Turing machine or lambda calculus. To better understand the implications of the Church-Turing thesis, we need to explore the different kinds of computational machines.

A chaos-modulated metasurface for physical-layer secure communications

With so many people using devices that can be connected to the internet, reliably securing wireless communications and protecting the data they are exchanging is of growing importance. While computer scientists have devised increasingly advanced security measures over the past decades, the most effective techniques rely on complex algorithms and intensive computations, which can consume a lot of energy.

Researchers at Peking University, Southeast University, University of Sannio and other institutes recently introduced a new approach for securing communications both effectively and energy-efficiently, which relies on a reconfigurable metasurface with properties that are modulated by chaotic patterns.

This approach, outlined in a paper published in Nature Communications, is based on an idea conceived by the senior authors Vincenzo Galdi, Lianlin Li and Tie Jun Cui, who oversaw the project. The idea was then realized at Peking University and Southeast University by junior authors JiaWen Xu Menglin Wei and Lei Zhang.