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Space & Physics

A New Milestone in Quantum Error Correction

This achievement moves quantum computing closer to becoming a transformative tool for science and technology

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Image credit: Pixabay

Quantum computing promises to revolutionize fields like cryptography, drug discovery, and optimization, but it faces a major hurdle: qubits, the fundamental units of quantum computers, are incredibly fragile. They are highly sensitive to external disturbances, making today’s quantum computers too error-prone for practical use. To overcome this, researchers have turned to quantum error correction, a technique that aims to convert many imperfect physical qubits into a smaller number of more reliable logical qubits.

In the 1990s, researchers developed the theoretical foundations for quantum error correction, showing that multiple physical qubits could be combined to create a single, more stable logical qubit. These logical qubits would then perform calculations, essentially turning a system of faulty components into a functional quantum computer. Michael Newman, a researcher at Google Quantum AI, highlights that this approach is the only viable path toward building large-scale quantum computers.

However, the process of quantum error correction has its limits. If physical qubits have a high error rate, adding more qubits can make the situation worse rather than better. But if the error rate of physical qubits falls below a certain threshold, the balance shifts. Adding more qubits can significantly improve the error rate of the logical qubits.

A Breakthrough in Error Correction

In a paper published in Nature last December, Michael Newman and his team at Google Quantum AI have achieved a major breakthrough in quantum error correction. They demonstrated that by adding physical qubits to a system, the error rate of a logical qubit drops sharply. This finding shows that they’ve crossed the critical threshold where error correction becomes effective. The research marks a significant step forward, moving quantum computers closer to practical, large-scale applications.

The concept of error correction itself isn’t new — it is already used in classical computers. On traditional systems, information is stored as bits, which can be prone to errors. To prevent this, error-correcting codes replicate each bit, ensuring that errors can be corrected by a majority vote. However, in quantum systems, things are more complicated. Unlike classical bits, qubits can suffer from various types of errors, including decoherence and noise, and quantum computing operations themselves can introduce additional errors.

Moreover, unlike classical bits, measuring a qubit’s state directly disturbs it, making it much harder to identify and correct errors without compromising the computation. This makes quantum error correction particularly challenging.

The Quantum Threshold

Quantum error correction relies on the principle of redundancy. To protect quantum information, multiple physical qubits are used to form a logical qubit. However, this redundancy is only beneficial if the error rate is low enough. If the error rate of physical qubits is too high, adding more qubits can make the error correction process counterproductive.

Google’s recent achievement demonstrates that once the error rate of physical qubits drops below a specific threshold, adding more qubits improves the system’s resilience. This breakthrough brings researchers closer to achieving large-scale quantum computing systems capable of solving complex problems that classical computers cannot.

Moving Forward

While significant progress has been made, quantum computing still faces many engineering challenges. Quantum systems require extremely controlled environments, such as ultra-low temperatures, and the smallest disturbances can lead to errors. Despite these hurdles, Google’s breakthrough in quantum error correction is a major step toward realizing the full potential of quantum computing.

By improving error correction and ensuring that more reliable logical qubits are created, researchers are steadily paving the way for practical quantum computers. This achievement moves quantum computing closer to becoming a transformative tool for science and technology.

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Space & Physics

MIT unveils an ultra-efficient 5G receiver that may supercharge future smart devices

A key innovation lies in the chip’s clever use of a phenomenon called the Miller effect, which allows small capacitors to perform like larger ones

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Image credit: Mohamed Hassan from Pixabay

A team of MIT researchers has developed a groundbreaking wireless receiver that could transform the future of Internet of Things (IoT) devices by dramatically improving energy efficiency and resilience to signal interference.

Designed for use in compact, battery-powered smart gadgets—like health monitors, environmental sensors, and industrial trackers—the new chip consumes less than a milliwatt of power and is roughly 30 times more resistant to certain types of interference than conventional receivers.

“This receiver could help expand the capabilities of IoT gadgets,” said Soroush Araei, an electrical engineering graduate student at MIT and lead author of the study, in a media statement. “Devices could become smaller, last longer on a battery, and work more reliably in crowded wireless environments like factory floors or smart cities.”

The chip, recently unveiled at the IEEE Radio Frequency Integrated Circuits Symposium, stands out for its novel use of passive filtering and ultra-small capacitors controlled by tiny switches. These switches require far less power than those typically found in existing IoT receivers.

A key innovation lies in the chip’s clever use of a phenomenon called the Miller effect, which allows small capacitors to perform like larger ones. This means the receiver achieves necessary filtering without relying on bulky components, keeping the circuit size under 0.05 square millimeters.

Credit: Courtesy of the researchers/MIT News

Traditional IoT receivers rely on fixed-frequency filters to block interference, but next-generation 5G-compatible devices need to operate across wider frequency ranges. The MIT design meets this demand using an innovative on-chip switch-capacitor network that blocks unwanted harmonic interference early in the signal chain—before it gets amplified and digitized.

Another critical breakthrough is a technique called bootstrap clocking, which ensures the miniature switches operate correctly even at a low power supply of just 0.6 volts. This helps maintain reliability without adding complex circuitry or draining battery life.

The chip’s minimalist design—using fewer and smaller components—also reduces signal leakage and manufacturing costs, making it well-suited for mass production.

Looking ahead, the MIT team is exploring ways to run the receiver without any dedicated power source—possibly by harvesting ambient energy from nearby Wi-Fi or Bluetooth signals.

The research was conducted by Araei alongside Mohammad Barzgari, Haibo Yang, and senior author Professor Negar Reiskarimian of MIT’s Microsystems Technology Laboratories.

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Society

Ahmedabad Plane Crash: The Science Behind Aircraft Take-Off -Understanding the Physics of Flight

Take-off is one of the most critical phases of flight, relying on the precise orchestration of aerodynamics, propulsion, and control systems. Here’s how it works:

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On June 12, 2025, a tragic aviation accident struck Ahmedabad, India when a regional passenger aircraft, Air India flight A1-171, crashed during take-off at Sardar Vallabhbhai Patel International Airport. According to preliminary reports, the incident resulted in over 200 confirmed casualties, including both passengers and crew members, and several others are critically injured. The aviation community and scientific world now turn their eyes not just toward the cause but also toward understanding the complex science behind what should have been a routine take-off.

How Do Aircraft Take Off?

Take-off is one of the most critical phases of flight, relying on the precise orchestration of aerodynamics, propulsion, and control systems. Here’s how it works:

1. Lift and Thrust

To leave the ground, an aircraft must generate lift, a force that counters gravity. This is achieved through the unique shape of the wing, called an airfoil, which creates a pressure difference — higher pressure under the wing and lower pressure above — according to Bernoulli’s Principle and Newton’s Third Law.

Simultaneously, engines provide thrust, propelling the aircraft forward. Most commercial jets use turbofan engines, which accelerate air through turbines to generate power.

2. Critical Speeds

Before takeoff, pilots calculate critical speeds:

  • V1 (Decision Speed): The last moment a takeoff can be safely aborted.
  • Vr (Rotation Speed): The speed at which the pilot begins to lift the nose.
  • V2 (Takeoff Safety Speed): The speed needed to climb safely even if one engine fails.

If anything disrupts this process — like bird strikes, engine failure, or runway obstructions — the results can be catastrophic.

Environmental and Mechanical Challenges

Factors like wind shear, runway surface condition, mechanical integrity, or pilot error can interfere with safe take-off. Investigators will be analyzing these very aspects in the Ahmedabad case.

The Bigger Picture

Take-off accounts for a small fraction of total flight time but is disproportionately associated with accidents — approximately 14% of all aviation accidents occur during take-off or initial climb.

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Space & Physics

MIT claims breakthrough in simulating physics of squishy, elastic materials

In a series of experiments, the new solver demonstrated its ability to simulate a diverse array of elastic behaviors, ranging from bouncing geometric shapes to soft, squishy characters

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Image credit: Courtesy of researchers

Researchers at MIT claim to have unveiled a novel physics-based simulation method that significantly improves stability and accuracy when modeling elastic materials — a key development for industries spanning animation, engineering, and digital fabrication.

In a series of experiments, the new solver demonstrated its ability to simulate a diverse array of elastic behaviors, ranging from bouncing geometric shapes to soft, squishy characters. Crucially, it maintained important physical properties and remained stable over long periods of time — an area where many existing methods falter.

Other simulation techniques frequently struggled in tests: some became unstable and caused erratic behavior, while others introduced excessive damping that distorted the motion. In contrast, the new method preserved elasticity without compromising reliability.

“Because our method demonstrates more stability, it can give animators more reliability and confidence when simulating anything elastic, whether it’s something from the real world or even something completely imaginary,” Leticia Mattos Da Silva, a graduate student at MIT’s Department of Electrical Engineering and Computer Science, said in a media statement.

Their study, though not yet peer-reviewed or published, will be presented at the August proceedings of the SIGGRAPH conference in Vancouver, Canada.

While the solver does not prioritize speed as aggressively as some tools, it avoids the accuracy and robustness trade-offs often associated with faster methods. It also sidesteps the complexity of nonlinear solvers, which are commonly used in physics-based approaches but are often sensitive and prone to failure.

Looking ahead, the research team aims to reduce computational costs and broaden the solver’s applications. One promising direction is in engineering and fabrication, where accurate elastic simulations could enhance the design of real-world products such as garments, medical devices, and toys.

“We were able to revive an old class of integrators in our work. My guess is there are other examples where researchers can revisit a problem to find a hidden convexity structure that could offer a lot of advantages,” Mattos Da Silva added.

The study opens new possibilities not only for digital content creation but also for practical design fields that rely on predictive simulations of flexible materials.

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