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Computer Programming

The Limits of Precision: How AI Can Help Us Reach the Edge of What Physics Allows

Scientists have uncovered how close we can get to perfect optical precision using AI, despite the physical limitations imposed by light itself. By combining physics theory with neural networks trained on distorted light patterns, they showed it’s possible to estimate object positions with nearly the highest accuracy allowed by nature. This breakthrough opens exciting new doors for applications in medical imaging, quantum tech, and materials science.

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The concept of precision has been a cornerstone in physics for centuries. For 150 years, it has been known that no matter how advanced our technology becomes, there are fundamental limits to the precision with which we can measure physical phenomena. The position of a particle, for instance, can never be measured with infinite precision; a certain amount of blurring is unavoidable.

Recently, an international team of researchers from TU Wien (Vienna), the University of Glasgow, and the University of Grenoble posed a question: where is the absolute limit of precision that is possible with optical methods? And how can this limit be approached as closely as possible? The team’s findings have significant implications for a wide range of fields, including medicine.

To address this question, the researchers employed a theoretical measure known as Fisher information. This measure describes how much information an optical signal contains about an unknown parameter – such as the object position. By using Fisher information, the team was able to calculate an upper limit for the theoretically achievable precision in different experimental scenarios.

However, the calculation of this limit does not necessarily mean that it is impossible to achieve. In fact, a corresponding experiment designed by Dorian Bouchet from the University of Grenoble, together with Ilya Starshynov and Daniele Faccio from the University of Glasgow, showed that using artificial intelligence (AI) algorithms for neural networks can come very close to this limit.

In the experiment, a laser beam was directed at a small, reflective object located behind a turbid liquid. The measurement conditions varied depending on the turbidity – and therefore also the difficulty of obtaining precise position information from the signal. The recorded images only showed highly distorted light patterns that looked like random patterns to the human eye.

But when fed into a neural network, which was trained with many such images each with a known object position, the network could learn which patterns are associated with which positions. After sufficient training, the network was able to determine the object position very precisely, even with new, unknown patterns.

The precision of the prediction achieved by the AI-supported algorithm was only minimally worse than the theoretically achievable maximum calculated using Fisher information. This means that our AI-supported algorithm is not only effective but almost optimal, achieving almost exactly the precision permitted by the laws of physics.

This realisation has far-reaching consequences: with the help of intelligent algorithms, optical measurement methods could be significantly improved in a wide range of areas – from medical diagnostics to materials research and quantum technology. In future projects, the research team wants to work with partners from applied physics and medicine to investigate how these AI-supported methods can be used in specific systems.

Artificial Intelligence

The Quantum Drumhead Revolution: A Breakthrough in Signal Transmission with Near-Perfect Efficiency

Researchers have developed an ultra-thin drumhead-like membrane that lets sound signals, or phonons, travel through it with astonishingly low loss, better than even electronic circuits. These near-lossless vibrations open the door to new ways of transferring information in systems like quantum computers or ultra-sensitive biological sensors.

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The Niels Bohr Institute at the University of Copenhagen has made a groundbreaking discovery that could revolutionize the way we transmit information. Researchers, in collaboration with the University of Konstanz and ETH Zurich, have successfully sent vibrations through an ultra-thin drumhead, measuring only 10 mm wide, with astonishingly low loss – just one phonon out of a million. This achievement is even more impressive than electronic circuit signal handling.

The drumhead, perforated with many triangular holes, utilizes the concept of phonons to transmit signals. Phonons are essentially sound waves that travel through solid materials by vibrating atoms and pushing each other. This phenomenon is not unlike encoding a message and sending it through a material, where signal loss can occur due to various factors like heat or incorrect vibrations.

The researchers’ success lies in achieving almost lossless transmission of signals through the membrane. The reliability of this platform for sending information is incredibly high, making it a promising candidate for future applications. To measure the loss, researchers directed the signal through the material and around the holes, observing that the amplitude decreased by only about one phonon out of a million.

This achievement has significant implications for quantum research. Building a quantum computer requires super-precise transfer of signals between its different parts. The development of sensors capable of measuring the smallest biological fluctuations in our own body also relies heavily on signal transfer. As Assistant Professor Xiang Xi and Professor Albert Schliesser explain, their current focus is on exploring further possibilities with this method.

“We want to experiment with more complex structures and see how phonons move around them or collide like cars at an intersection,” says Albert Schliesser. “This will give us a better understanding of what’s ultimately possible and what new applications there are.” The pursuit of basic research is about producing new knowledge, and this discovery is a testament to the power of scientific inquiry.

In conclusion, the quantum drumhead revolution has brought us one step closer to achieving near-perfect signal transmission. As researchers continue to explore the possibilities of this method, we can expect exciting breakthroughs in various fields, ultimately leading to innovative applications that will transform our understanding of the world.

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Computer Programming

AI Revolutionizes Heart Risk Prediction, Saving Lives and Reducing Unnecessary Interventions

An advanced Johns Hopkins AI model called MAARS combs through underused heart MRI scans and complete medical records to spot hidden scar patterns that signal sudden cardiac death, dramatically outperforming current dice-roll clinical guidelines and promising to save lives while sparing patients unnecessary defibrillators.

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The AI model significantly outperformed traditional clinical guidelines, achieving an accuracy rate of 89% across all patients, with a remarkable 93% accuracy for individuals between 40-60 years old – the population most at-risk for sudden cardiac death. By accurately predicting patient risk, doctors can tailor medical plans to suit individual needs, reducing unnecessary interventions and saving lives.

Led by researcher Natalia Trayanova, the team’s findings were published in Nature Cardiovascular Research. The study demonstrates the potential of AI to transform clinical care, particularly in high-risk areas such as sudden cardiac death prediction. With further testing and expansion to other heart diseases, this technology has the potential to save many lives and improve patient outcomes.

In an interview, Trayanova noted that current clinical guidelines for identifying patients at risk have about a 50% chance of success – “not much better than throwing dice.” The AI model’s accuracy is a significant improvement, with Trayanova stating that it can predict with high accuracy whether a patient is at very high risk for sudden cardiac death or not.

The team tested the MAARS model against real patients treated with traditional clinical guidelines at Johns Hopkins Hospital and Sanger Heart & Vascular Institute in North Carolina. The results showed that the AI model was more accurate than human clinicians, with an impressive 93% accuracy rate for individuals between 40-60 years old.

The study’s co-author, Jonathan Crispin, a Johns Hopkins cardiologist, stated that the research demonstrates the potential of AI to transform clinical care and enhance patient outcomes. The team plans to further test the MAARS model on more patients and expand its use to other heart diseases, including cardiac sarcoidosis and arrhythmogenic right ventricular cardiomyopathy.

The development of this AI model offers a glimmer of hope for those affected by hypertrophic cardiomyopathy and sudden cardiac death, providing a new tool for doctors to accurately predict patient risk and tailor medical plans accordingly. As the research continues to evolve, it has the potential to save many lives and improve patient outcomes worldwide.

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Computer Modeling

Scientists Crack Code to Simulate Quantum Computations, Paving Way for Robust Quantum Computers

A multinational team has cracked a long-standing barrier to reliable quantum computing by inventing an algorithm that lets ordinary computers faithfully mimic a fault-tolerant quantum circuit built on the notoriously tricky GKP bosonic code, promising a crucial test-bed for future quantum hardware.

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The researchers have successfully simulated quantum computations with an error correction code known as the Gottesman-Kitaev-Preskill (GKP) code. This code is commonly used in leading implementations of quantum computers and allows for the correction of errors without destroying the quantum information.

The method developed by the researchers consists of an algorithm capable of simulating quantum computations using a bosonic code, specifically the GKP code. This achievement has been deemed impossible until now due to the immense complexity of quantum computations.

“We have discovered a way to simulate a specific type of quantum computation where previous methods have not been effective,” says Cameron Calcluth, PhD in Applied Quantum Physics at Chalmers and first author of the study published in Physical Review Letters. “This means that we can now simulate quantum computations with an error correction code used for fault tolerance, which is crucial for being able to build better and more robust quantum computers in the future.”

The researchers’ breakthrough has far-reaching implications for the development of stable and scalable quantum computers, which are essential for solving complex problems in various fields. The new method will enable researchers to test and validate a quantum computer’s calculations more reliably, paving the way for the creation of truly reliable quantum computers.

The article Classical simulation of circuits with realistic odd-dimensional Gottesman-Kitaev-Preskill states has been published in Physical Review Letters. The authors are Cameron Calcluth, Giulia Ferrini, Oliver Hahn, Juani Bermejo-Vega, and Alessandro Ferraro.

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