The University of British Columbia (UBC) researchers have proposed a groundbreaking solution to overcome the hurdles in quantum networking. They’ve designed a device that can efficiently convert microwave signals into optical signals and vice versa, which is crucial for transmitting information across cities or continents through fibre optic cables.

This “universal translator” for quantum computers is remarkable because it preserves the delicate entangled connections between distant particles, allowing them to remain connected despite distance. Losing this connection means losing the quantum advantage that enables tasks like creating unbreakable online security and predicting weather with improved accuracy.

The team’s breakthrough lies in tiny engineered flaws, magnetic defects intentionally embedded in silicon to control its properties. When microwave and optical signals are precisely tuned, electrons in these defects convert one signal to the other without absorbing energy, avoiding the instability that plagues other transformation methods.

This device is impressive because it can efficiently run at extremely low power – just millionths of a watt – using superconducting components alongside this specially engineered silicon. The authors have outlined a practical design for mass production, which could lead to widespread adoption in existing communication infrastructure.

While we’re not getting a quantum internet tomorrow, this discovery clears a major roadblock. UBC researchers hope that their approach will change the game by enabling reliable long-distance quantum information transmission between cities. This could pave the way for breakthroughs like unbreakable online security, GPS working indoors, and solving complex problems like designing new medicines or predicting weather with improved accuracy.

The implications of this research are vast, and it’s an exciting time to see how scientists will build upon this discovery to further advance our understanding of quantum technology.

Imagine a world where computers can process information at incredible velocities, far surpassing today’s electronic systems. A groundbreaking study has made significant strides in achieving this vision by utilizing glass fibers to perform tasks faster and more efficiently. This novel approach involves harnessing the power of light to mimic artificial intelligence (AI) processes, leveraging nonlinear interactions between intense laser pulses and thin glass fibers.

The research collaboration between postdoctoral researchers Dr. Mathilde Hary from Tampere University in Finland and Dr. Andrei Ermolaev from the Université Marie et Louis Pasteur in France has successfully demonstrated a particular class of computing architecture known as an Extreme Learning Machine (ELM), inspired by neural networks.

Unlike traditional electronics, which approach their limits in terms of bandwidth, data throughput, and power consumption, optical fibers can transform input signals at speeds thousands of times faster. By confining light within glass fibers to areas smaller than a fraction of human hair, the researchers have achieved remarkable results.

Their study has used femtosecond laser pulses (a billion times shorter than a camera flash) to encode information into the fiber. This approach not only classifies handwritten digits with an accuracy rate of over 91% but also does so in under one picosecond – a feat rivaling state-of-the-art digital methods.

What’s remarkable about this achievement is that the best results didn’t occur at maximum levels of nonlinear interaction or complexity, but rather from a delicate balance between fiber length, dispersion, and power levels. According to Dr. Hary, “Performance is not simply a matter of pushing more power through the fiber; it depends on how precisely the light is initially structured, in other words, how information is encoded, and how it interacts with the fiber properties.”

This groundbreaking research has opened doors to new ways of computing while exploring routes towards more efficient architectures. By harnessing the potential of light, scientists can pave the way for ultra-fast computers that not only process information at incredible velocities but also reduce energy consumption.

The collaboration between Tampere University and Université Marie et Louis Pasteur is a testament to the power of interdisciplinary research in advancing optical nonlinearity through AI and photonics. This work demonstrates how fundamental research in nonlinear fiber optics can drive new approaches to computation, merging physics and machine learning to open new paths toward ultrafast and energy-efficient AI hardware.

As researchers continue to explore this innovative technology, potential applications range from real-time signal processing to environmental monitoring and high-speed AI inference. With funding from the Research Council of Finland, the French National Research Agency, and the European Research Council, this project is poised to revolutionize the computing landscape and unlock new possibilities for humanity.

The cement industry produces around eight percent of global CO2 emissions – more than the entire aviation sector worldwide. Researchers at the Paul Scherrer Institute PSI have developed an AI-based model that helps to accelerate the discovery of new cement formulations that could yield the same material quality with a better carbon footprint.

The rotary kilns in cement plants are heated to a scorching 1,400 degrees Celsius to burn ground limestone down to clinker, the raw material for ready-to-use cement. Unsurprisingly, such temperatures typically can’t be achieved with electricity alone. They are the result of energy-intensive combustion processes that emit large amounts of carbon dioxide (CO2). What may be surprising, however, is that the combustion process accounts for less than half of these emissions, far less. The majority is contained in the raw materials needed to produce clinker and cement: CO2 that is chemically bound in the limestone is released during the production process.

To address this issue, researchers at PSI have developed an AI-powered tool that can identify optimal cement formulations with lower CO2 emissions and higher material quality. This tool uses a combination of machine learning algorithms and genetic programming to search for the best recipe based on user-defined specifications.

The study was conducted as part of the SCENE project, an interdisciplinary research program aimed at reducing greenhouse gas emissions in industry and energy supply. The researchers involved came from various disciplines, including cement chemistry, thermodynamics, and AI specialization.

The results show that the AI-powered tool can identify promising formulations with real potential for reducing CO2 emissions and improving material quality. However, further testing is required to confirm these findings and ensure practical feasibility in production.

Some of the key takeaways from this study include:

* The cement industry produces around 8% of global CO2 emissions.
* The majority of these emissions come from raw materials rather than combustion processes.
* An AI-powered tool can identify optimal cement formulations with lower CO2 emissions and higher material quality.
* Interdisciplinary collaboration is essential for developing effective solutions to complex problems like this one.

Overall, the study highlights the potential of AI-powered tools in addressing sustainability challenges and improving material quality. As the demand for more sustainable materials continues to grow, researchers and industry professionals will likely continue to explore innovative solutions like this one.