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Computer Programming

Boosting AI with Green Quantum Chips: A Breakthrough in Photonic Quantum Computing

A team of researchers has shown that even small-scale quantum computers can enhance machine learning performance, using a novel photonic quantum circuit. Their findings suggest that today s quantum technology isn t just experimental it can already outperform classical systems in specific tasks. Notably, this photonic approach could also drastically reduce energy consumption, offering a sustainable path forward as machine learning s power needs soar.

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The integration of artificial intelligence (AI) and quantum computing has been a topic of intense research in recent years. A team of international researchers from the University of Vienna has made a significant breakthrough in this field by demonstrating that small-scale quantum computers can enhance the performance of machine learning algorithms. Their study, published in Nature Photonics, showcases promising applications for optical quantum computers.

Machine learning and AI have revolutionized our lives with their ability to perform complex tasks and drive scientific research. Quantum computing, on the other hand, has emerged as a new paradigm for computation. The combination of these two fields has given rise to the field of Quantum Machine Learning, which aims to find enhancements in speed, efficiency, or accuracy when running algorithms on quantum platforms.

However, achieving such advantages with current technology is still an open challenge. The University of Vienna team took this next step by designing a novel experiment featuring a photonic quantum circuit and a machine learning algorithm. Their goal was to classify data points using a photonic quantum computer and understand the contribution of quantum effects in comparison to classical computers.

The results were promising, as they found that already small-sized quantum processors can perform better than conventional algorithms. “We found that for specific tasks our algorithm commits fewer errors than its classical counterpart,” explained Philip Walther from the University of Vienna, lead of the project. This implies that existing quantum computers can show good performances without necessarily going beyond state-of-the-art technology.

Another significant aspect of this research is that photonic platforms can consume less energy compared to standard computers. Given the high energy demands of machine learning algorithms, this could prove crucial in the future. Co-author Iris Agresti emphasized that new algorithms inspired by quantum architectures could be designed, reaching better performances and reducing energy consumption.

This breakthrough has a significant impact on both quantum computation and standard computing. It identifies tasks that benefit from quantum effects and opens up possibilities for designing more efficient and eco-friendly algorithms. The integration of AI and quantum computing is an exciting area of research, and this study takes us one step closer to making AI smarter and greener.

Computational Biology

A Quantum Leap Forward – New Amplifier Boosts Efficiency of Quantum Computers 10x

Chalmers engineers built a pulse-driven qubit amplifier that’s ten times more efficient, stays cool, and safeguards quantum states—key for bigger, better quantum machines.

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Quantum computers have long been touted as revolutionary machines capable of solving complex problems that stymie conventional supercomputers. However, their full potential has been hindered by the limitations of qubit amplifiers – essential components required to read and interpret quantum information. Researchers at Chalmers University of Technology in Sweden have taken a significant step forward with the development of an ultra-efficient amplifier that reduces power consumption by 90%, paving the way for more powerful quantum computers with enhanced performance.

The new amplifier is pulse-operated, meaning it’s activated only when needed to amplify qubit signals, minimizing heat generation and decoherence. This innovation has far-reaching implications for scaling up quantum computers, as larger systems require more amplifiers, leading to increased power consumption and decreased accuracy. The Chalmers team’s breakthrough offers a solution to this challenge, enabling the development of more accurate readout systems for future generations of quantum computers.

One of the key challenges in developing pulse-operated amplifiers is ensuring they respond quickly enough to keep pace with qubit readout. To address this, the researchers employed genetic programming to develop a smart control system that enables rapid response times – just 35 nanoseconds. This achievement has significant implications for the future of quantum computing, as it paves the way for more accurate and powerful calculations.

The new amplifier was developed in collaboration with industry partners Low Noise Factory AB and utilizes the expertise of researchers at Chalmers’ Terahertz and Millimeter Wave Technology Laboratory. The study, published in IEEE Transactions on Microwave Theory and Techniques, demonstrates a novel approach to developing ultra-efficient amplifiers for qubit readout and offers promising prospects for future research.

In conclusion, the development of this highly efficient amplifier represents a significant leap forward for quantum computing. By reducing power consumption by 90%, researchers have opened doors to more powerful and accurate calculations, unlocking new possibilities in fields such as drug development, encryption, AI, and logistics. As the field continues to evolve, it will be exciting to see how this innovation shapes the future of quantum computing.

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Artificial Intelligence

AI Uncovers Hidden Heart Risks in CT Scans: A Game-Changer for Cardiovascular Care

What if your old chest scans—taken years ago for something unrelated—held a secret warning about your heart? A new AI tool called AI-CAC, developed by Mass General Brigham and the VA, can now comb through routine CT scans to detect hidden signs of heart disease before symptoms strike.

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The Massachusetts General Brigham researchers have developed an innovative artificial intelligence (AI) tool called AI-CAC to analyze previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels, indicating a greater risk for cardiovascular events. Their research, published in NEJM AI, demonstrated the high accuracy and predictive value of AI-CAC for future heart attacks and 10-year mortality.

Millions of chest CT scans are taken each year, often in healthy people, to screen for lung cancer or other conditions. However, this study reveals that these scans can also provide valuable information about cardiovascular risk, which has been going unnoticed. The researchers found that AI-CAC had a high accuracy rate (89.4%) at determining whether a scan contained CAC or not.

The gold standard for quantifying CAC uses “gated” CT scans, synchronized to the heartbeat to reduce motion during the scan. However, most chest CT scans obtained for routine clinical purposes are “nongated.” The researchers developed AI-CAC, a deep learning algorithm, to probe through these nongated scans and quantify CAC.

The AI-CAC model was 87.3% accurate at determining whether the score was higher or lower than 100, indicating a moderate cardiovascular risk. Importantly, AI-CAC was also predictive of 10-year all-cause mortality, with those having a CAC score over 400 having a 3.49 times higher risk of death over a 10-year period.

The researchers hope to conduct future studies in the general population and test whether the tool can assess the impact of lipid-lowering medications on CAC scores. This could lead to the implementation of AI-CAC in clinical practice, enabling physicians to engage with patients earlier, before their heart disease advances to a cardiac event.

As Dr. Raffi Hagopian, first author and cardiologist at the VA Long Beach Healthcare System, emphasized, “Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease, reducing long-term morbidity, mortality, and healthcare costs.”

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Computer Modeling

Harnessing True Randomness from Entangled Photons: The Colorado University Randomness Beacon (CURBy)

Scientists at NIST and the University of Colorado Boulder have created CURBy, a cutting-edge quantum randomness beacon that draws on the intrinsic unpredictability of quantum entanglement to produce true random numbers. Unlike traditional methods, CURBy is traceable, transparent, and verifiable thanks to quantum physics and blockchain-like protocols. This breakthrough has real-world applications ranging from cybersecurity to public lotteries—and it’s open source, inviting the world to use and build upon it.

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The Colorado University Randomness Beacon (CURBy) is a pioneering service that harnesses the true randomness of entangled photons to produce unguessable strings of numbers. This breakthrough was made possible by the work of scientists at the National Institute of Standards and Technology (NIST) and their colleagues at the University of Colorado Boulder.

“True randomness is something that nothing in the universe can predict in advance,” said Krister Shalm, a physicist at NIST. “If God does play dice with the universe, then you can turn that into the best random number generator that the universe allows.”

The CURBy system uses a Bell test to measure pairs of entangled photons whose properties are correlated even when separated by vast distances. When researchers measure an individual particle, the outcome is random, but the properties of the pair are more correlated than classical physics allows, enabling researchers to verify the randomness.

This is the first random number generator service to use quantum nonlocality as a source of its numbers, and the most transparent source of random numbers to date. The results are certifiable and traceable to a greater extent than ever before.

The CURBy system consists of a nonlinear crystal that generates entangled photons, which travel via optical fiber to separate labs at opposite ends of the hall. Once the photons reach the labs, their polarizations are measured. The outcomes of these measurements are truly random.

NIST passes millions of these quantum coin flips to a computer program at the University of Colorado Boulder, where special processing steps and strict protocols are used to turn the outcomes into 512 random bits of binary code (0s and 1s). The result is a set of random bits that no one, not even Einstein, could have predicted.

The CURBy system has been operational for several months now, with an impressive success rate of over 99.7%. The ability to verify the data behind each random number was made possible by the Twine protocol, a novel set of quantum-compatible blockchain technologies developed by NIST and its collaborators.

“The Twine protocol lets us weave together all these other beacons into a tapestry of trust,” said Jasper Palfree, a research assistant on the project at the University of Colorado Boulder. This allows any user to verify the data behind each random number, providing security and traceability.

The CURBy system can be used anywhere an independent, public source of random numbers would be useful, such as selecting jury candidates, making a random selection for an audit, or assigning resources through a public lottery.

“I wanted to build something that is useful. It’s this cool thing that is the cutting edge of fundamental science,” said Gautam Kavuri, a graduate student on the project. The whole process is open source and available to the public, allowing anyone to not only check their work but even build on the beacon to create their own random number generator.

The CURBy system has the potential to revolutionize fields such as cryptography, gaming, and finance, where true randomness is essential. By harnessing the power of entangled photons, scientists have created a truly independent source of random numbers that can be trusted.

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