A team of researchers from the Graduate School of Engineering Science at Osaka University has made a groundbreaking discovery that could bring quantum computers one step closer to reality. In an article published in PRX Quantum, they have developed a method for preparing high-fidelity “magic states” with unprecedented accuracy and significantly less overhead.

Quantum computers are machines that harness the unique properties of quantum mechanics to perform calculations at speeds millions of times faster than classical computers. These machines could revolutionize fields like engineering, finance, and biotechnology. However, there’s been a significant obstacle holding them back: noise.

Noise is an enemy of quantum computers because even the slightest disturbance can ruin a setup, making it useless. To overcome this challenge, scientists have been exploring ways to build fault-tolerant quantum computers that can continue computing accurately despite noise.

One popular method for creating such systems is called magic state distillation. This process involves preparing a single high-fidelity quantum state from many noisy ones. However, traditional magic state distillation is computationally expensive and requires many qubits (the basic units of quantum information).

The research team was inspired to create a new version of magic state distillation, which they call “level-zero.” In this approach, a fault-tolerant circuit is developed at the physical level of qubits, rather than higher, more abstract levels. This innovation has led to a significant decrease in spatial and temporal overhead compared to traditional methods.

According to lead researcher Tomohiro Itogawa, this breakthrough could bring quantum computers closer to reality: “Noise is absolutely the number one enemy of quantum computers. We’re optimistic that our technique will help make large-scale quantum computers more feasible.”

Keisuke Fujii, senior author of the study, added: “We wanted to explore if there was any way of expediting the preparation of high-fidelity states necessary for quantum computation. Our results show that this is indeed possible, and we’re excited about the potential implications.”

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.