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Computer Science

The Dark Side of Green Energy: Understanding the High Construction Risks of Sustainable Infrastructure Projects

The average energy project costs 40% more than expected for construction and takes almost two years longer than planned, finds a new global study. One key insight: The investment risk is highest for nuclear power plant construction and lowest for solar. The researchers analyzed data from 662 energy projects built between 1936 and 2024 in 83 countries, totaling $1.358 trillion in investment.

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The International Energy Agency projects that more than $100 trillion will be spent on building net-zero energy infrastructure globally between now and 2050. However, every single one of these projects runs the risk of higher-than-expected construction costs or time delays. A new state-of-the-art study published in the journal Energy Research & Social Science has found that runaway construction costs and delayed timelines stymie many energy projects.

According to the study, nuclear power plants are the worst offenders, with an average construction cost overrun of 102.5% and ending up costing $1.56 billion more than expected. Hydrogen infrastructure and carbon capture and storage both exhibit significant average time and cost overruns for construction, along with thermal power plants relying on natural gas.

In contrast, solar energy and electricity grid transmission projects have the best construction track record and are often completed ahead of schedule or below expected cost. Wind farms also performed favorably in the financial risk assessment.

The researchers compiled data on 662 energy infrastructure projects covering a diverse spectrum of technology classes and capacities, built between 1936 and 2024 across 83 countries, representing $1.358 trillion in investment. The study evaluated ten types of projects, including emerging innovations such as geothermal and bioenergy.

Understanding what causes energy projects to go over budget and fall behind schedule — and when that tipping point occurs — is another important contribution of this global analysis. The study examined diseconomies of scale, construction delays, and governance factors to identify critical thresholds when project costs surge.

The findings suggest that smaller, modular renewable projects might not only bring environmental benefits but also potentially reduce financial risk and offer better budget predictability.

The researchers’ conclusions are clear: “Low-carbon sources of energy such as wind and solar not only have huge climatic and energy security benefits, but also financial advantages related to less construction risk and less chance of delays.” The study provides fresh insights into the cost dynamics of these recently commercialized technologies.

This global analysis has important implications for better risk management strategies in energy infrastructure planning. As we commit trillions to global decarbonization efforts, it’s essential to consider the diseconomies of scale and potential risks associated with large-scale energy projects.

Computer Graphics

Cracking the Code: Scientists Breakthrough in Quantum Computing with a Single Atom

A research team has created a quantum logic gate that uses fewer qubits by encoding them with the powerful GKP error-correction code. By entangling quantum vibrations inside a single atom, they achieved a milestone that could transform how quantum computers scale.

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Scientists have achieved a major breakthrough in quantum computing by successfully cracking the code hidden within a single atom. To build a large-scale quantum computer that works, scientists and engineers need to overcome the spontaneous errors that quantum bits, or qubits, create as they operate.

The team at the Quantum Control Laboratory at the University of Sydney Nano Institute has demonstrated a type of quantum logic gate that drastically reduces the number physical qubits needed for its operation. They built an entangling logic gate on a single atom using an error-correcting code nicknamed the ‘Rosetta stone’ of quantum computing.

This curiously named Gottesman-Kitaev-Preskill (GKP) code has long offered a theoretical possibility for significantly reducing the physical number of qubits needed to produce a functioning ‘logical qubit.’ Albeit by trading efficiency for complexity, making the codes very difficult to control. The research published in Nature Physics demonstrates this as a physical reality.

Led by Sydney Horizon Fellow Dr Tingrei Tan at the University of Sydney Nano Institute, scientists have used their exquisite control over the harmonic motion of a trapped ion to bridge the coding complexity of GKP qubits, allowing a demonstration of their entanglement.

The team’s experiment has shown the first realization of a universal logical gate set for GKP qubits. They did this by precisely controlling the natural vibrations or harmonic oscillations of a trapped ion in such a way that they can manipulate individual GKP qubits or entangle them as a pair.

A logic gate is an information switch that allows computers – quantum and classical – to be programmable to perform logical operations. Quantum logic gates use the entanglement of qubits to produce a completely different sort of operational system to that used in classical computing, underpinning the great promise of quantum computers.

The researchers have effectively stored two error-correctable logical qubits in a single trapped ion and demonstrated entanglement between them using quantum control software developed by Q-CTRL. This result massively reduces the quantum hardware required to create these logic gates, which allow quantum machines to be programmed.

This research represents an important demonstration that quantum logic gates can be developed with a reduced physical number of qubits, increasing their efficiency. The authors declare no competing interests. Funding was received from various sources including the Australian Research Council and private funding from H. and A. Harley.

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Civil Engineering

A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Researchers have unveiled a new quantum material that could make quantum computers much more stable by using magnetism to protect delicate qubits from environmental disturbances. Unlike traditional approaches that rely on rare spin-orbit interactions, this method uses magnetic interactions—common in many materials—to create robust topological excitations. Combined with a new computational tool for finding such materials, this breakthrough could pave the way for practical, disturbance-resistant quantum computers.

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The article you provided was well-written, but I made some adjustments to improve clarity, structure, and style for general readers. Here’s the rewritten content:

A Groundbreaking Magnetic Trick for Quantum Computing: Stabilizing Qubits with Exotic Materials

Quantum computers have long been touted as revolutionaries in solving complex problems that conventional supercomputers can’t handle. However, their development has been hindered by one major challenge: qubits, the basic units of quantum computers, are extremely delicate and prone to losing their quantum states due to external disturbances.

Researchers from Chalmers University of Technology in Sweden and Aalto University and the University of Helsinki in Finland have now made a groundbreaking discovery that could change this. They’ve developed a new type of exotic quantum material that exhibits robust topological excitations, which are significantly more stable and resilient than other quantum states.

This breakthrough is an important step towards realising practical topological quantum computing by constructing stability directly into the material’s design. The researchers’ innovative approach uses magnetism as the key ingredient to achieve this effect, harnessing magnetic interactions to engineer robust topological excitations in a broader spectrum of materials.

“The advantage of our method is that magnetism exists naturally in many materials,” explains Guangze Chen, postdoctoral researcher in applied quantum physics at Chalmers and lead author of the study published in Physical Review Letters. “You can compare it to baking with everyday ingredients rather than using rare spices. This means that we can now search for topological properties in a much broader spectrum of materials, including those that have previously been overlooked.”

To accelerate the discovery of new materials with useful topological properties, the research team has also developed a new computational tool that can directly calculate how strongly a material exhibits topological behavior.

“Our hope is that this approach can help guide the discovery of many more exotic materials,” says Guangze Chen. “Ultimately, this can lead to next-generation quantum computer platforms, built on materials that are naturally resistant to the kind of disturbances that plague current systems.”

This magnetic trick has the potential to revolutionize the development of practical topological quantum computing and pave the way for next-generation quantum computer platforms. As researchers continue to explore and develop new exotic materials with robust topological excitations, we may finally see the dawn of a new era in quantum computing.

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Artificial Intelligence

“Revolutionizing Computing with the ‘Microwave Brain’ Chip”

Cornell engineers have built the first fully integrated “microwave brain” — a silicon microchip that can process ultrafast data and wireless signals at the same time, while using less than 200 milliwatts of power. Instead of digital steps, it uses analog microwave physics for real-time computations like radar tracking, signal decoding, and anomaly detection. This unique neural network design bypasses traditional processing bottlenecks, achieving high accuracy without the extra circuitry or energy demands of digital systems.

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The world of computing has taken a significant leap forward with the development of the “microwave brain” chip, a low-power microchip that can compute on both ultrafast data signals and wireless communication signals. This revolutionary innovation, created by researchers at Cornell University, marks the first time a processor has harnessed the physics of microwaves to perform real-time frequency domain computation.

Detailed in the journal Nature Electronics, this groundbreaking processor is the first true microwave neural network and is fully integrated on a silicon microchip. It can handle tasks like radio signal decoding, radar target tracking, and digital data processing while consuming less than 200 milliwatts of power – an impressive feat considering its speed and efficiency.

The secret behind this technology lies in its design as a neural network, modeled after the human brain’s interconnected modes produced in tunable waveguides. This allows it to recognize patterns and learn from data, unlike traditional digital computers that rely on step-by-step instructions timed by a clock. The microwave brain processor uses analog, nonlinear behavior in the microwave regime to handle data streams at speeds of tens of gigahertz – far faster than most digital chips.

“We’ve created something that looks more like a controlled mush of frequency behaviors that can ultimately give you high-performance computation,” says Alyssa Apsel, professor of engineering and co-senior author. Bal Govind, lead author and doctoral student, explains that the chip’s programmable distortion across a wide band of frequencies allows it to be repurposed for several computing tasks.

The microwave brain processor has achieved remarkable accuracy on multiple classification tasks involving wireless signal types, comparable to digital neural networks but with a fraction of the power and size. It can perform both low-level logic functions and complex tasks like identifying bit sequences or counting binary values in high-speed data.

With its extreme sensitivity to inputs, this chip is well-suited for hardware security applications like sensing anomalies in wireless communications across multiple bands of microwave frequencies. The researchers are optimistic about the scalability of this technology and are experimenting with ways to improve its accuracy and integrate it into existing microwave and digital processing platforms.

As the world becomes increasingly dependent on data-driven technologies, innovations like the microwave brain chip have the potential to revolutionize computing and redefine what is possible in the realm of artificial intelligence and machine learning.

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