The world’s largest cryptocurrency exchange Binance has been hit with the biggest ever cryptocurrency heist, in which billions of dollars of reserves were drained in just 20 minutes.

From 8:05pm on 29 October, $6.36 billion in cryptocurrencies were shifted from wallets in a coordinated attack from a North Korean-linked hacking unit called TraderTraitor. This group was of the Bybit crypto exchange in February 2025.

The US Treasury has that such stolen funds are used to finance North Korea’s nuclear missile programme.

Despite cryptocurrency’s founding idea of decentralisation, meaning no one has singular control over the money flowing through the network, there are some large single points of failure. It is thought one of these was the cause of the Binance hack, which gave access to a system the exchange uses to authorise transfers out of customers’ accounts.

In , Binance said its early investigations indicate the security incident may stem from a compromised laptop belonging to an engineer. TRM Labs, a cybersecurity firm, suggests that preparations for the attack may have begun several days ago.

Binance claims it has found no evidence that the private cryptographic keys used to authorise transactions were stolen. Instead, the attackers appear to have gained access to the software that requests withdrawals, potentially allowing them to instruct Binance’s own systems to approve the transfers. However, the investigation is continuing and the firm has said in there may have been “additional points of breach”.

“It shouldn’t be possible for a single [developer] PC to be compromised and somehow for that to allow the entire exchange to be compromised,” says at the University of Surrey, UK. “I don’t understand how that can happen.”

If that is the case, it highlights how precarious major financial hubs in the crypto world can be to single points of failure.

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“You can build a vault with mathematical firewalls, but if the individual holding the keys can be duped, the vault is opened,” says at FARx, a cybersecurity company. “Most crypto platforms are as centralised as any bank, with a small number of privileged individuals controlling billions. The difference is that once crypto is transferred, it’s gone.”

That centralisation can be a blessing as well as a curse, though: cryptocurrencies Tether and Circle have been able to freeze billions of dollars of stolen coins, stymying the hackers in attempts to transfer about half the stolen funds any further.

Woodward says the Binance incident potentially strikes a distinction between crypto security and more traditional finance. “You would hope there’d be things like two-man rules [where two separate people have to approve a change] or things that banks have for changing critical systems, but it doesn’t look like those were in place.”

Imagine if, in addition to sharing photos of those muffins you just baked, you could share their delicious aroma, too. That’s the promise of an AI-assisted device that can transmit smells over the internet. So far, it works on only a small number of scents, including orange, beer and cake, but its creators hope the system will ultimately recreate more smells. The scent of a pine forest or the aroma of coffee, for example, could then add a new dimension to virtual worlds and long-distance communication.

Although it is now routine to communicate sight, sound and even touch sensations electronically, smell is “still a very undeveloped sense”, says at the Ohio State University. That’s partly because odours involve complex chemistry. Whereas vision, for example, can be broken down into combinations of red, green and blue pixels, says Yang, there’s no simple building block underlying smell. Any attempt to convert aromas into electronic signals has to recognise combinations of very different molecules.

To solve the problem, Yang and her colleagues built an electronic nose, or “e-nose”, as well as a smell generator, then used machine learning to connect the two. The e-nose consists of arrays of sensors made of metal oxide. When different gaseous components of smells, such as ethanol (from alcoholic drinks) or limonene (found in citrus fruits), bind to the sensors’ surface, this changes their resistance, creating different electrical signals.

The researchers then trained an AI algorithm to recognise the distinctive signals created by smells from five different foods or beverages: orange, cake, coffee, beer and red wine. Teaching the AI to correctly recognise the smells was “the most challenging part” of the entire study, says Yang, because to generate enough data to train the system, she had to expose the sensors to the various smells hundreds of times.

This information was then sent over the internet to a smell generation device that the researchers are calling “Scentronic”. This part of the system involves miniature containers holding liquids laced with each of the five smells. Above each container is an atomiser that vibrates in response to electric signals, forcing the liquid in the container through a cotton filter and releasing fine droplets into the air, creating the appropriate smell, or combination of smells.

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Yang built several different designs including stringing the atomisers onto a necklace and a wristband, and mounting them on a handheld device. When she tested the handheld device on 14 volunteers, they correctly identified the smells being transmitted to them 73 per cent of the time, on average.

She also built a prototype that reproduces taste, involving a cup of water with electromagnetic pumps containing flavoured liquid fixed around its edge. “When it receives information from the internet, the corresponding liquid will be dropped into the cup.”

Alternative methods to generate smells remotely have been suggested, including to release particular scents, and even to create the experience of smell. But Yang argues her method is both fast and practical. She now hopes to expand the repertoire of the system, to create more complex aromas.

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She suggests the entertainment industry could be first to develop the technology, to create more immersive virtual worlds. But there could also be health applications. For instance, the smell of food transforms the eating experience, so the technology could be adapted to make soldiers’ or astronauts’ rations more palatable. The Scentronic system could also enable doctors to assess distortions in patients’ sense of smell, for example, in post-viral illness or neurodegenerative disease.

The Scentronic system is “a great idea” and a step in the right direction, says at University College London, although she points out that recognising a smell and then releasing a matching one from a limited library is very different from the ease with which we can transmit sound and vision. “There’s still a long way to go before we can truly record and replay smell experiences.”

Making quantum computers error-proof is one of the biggest challenges currently facing the technology. Researchers at Google Quantum AI have now demonstrated one way to do so more efficiently. Their work suggests the smaller quantum computers that will be available in the next few years could be more impactful than expected.

Quantum computers can run mathematical operations, analogous to addition or multiplication, that conventional computers cannot. This makes them suitable for solving some problems that had previously been considered unsolvable, such as those that underpin cryptography.

Most important among the quantum operations the computers run are “non-Clifford gates”, which are the key ingredients for algorithms that transcend conventional computation.

But there is a problem. Quantum computers are very error-prone, and their building blocks, called qubits, must be encoded into special groups, or “logical qubits”, before they can execute operations reliably. Executing non-Clifford gates in an error-corrected way requires large numbers of qubits, which means having to build very large quantum computers – a formidable engineering challenge.

Now, at Google Quantum AI and her colleagues have used the firm’s Willow quantum computer to show that the error-correction process can be made more efficient. Their new technique ultimately reduced Willow’s errors 40 times.

At the centre of their advance is the idea of a “magic state”, which is a special quantum state that qubits must assume before the quantum computer can run error-corrected non-Clifford operations. Creating these states had, so far, been a real bottleneck for quantum computing, says Rosenfeld. “It eats up a lot of qubits and a lot of time,” she says.

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In 2024, her colleagues at Google Quantum AI proposed a more efficient method for creating magic states called “magic state cultivation”. Here, instead of many imperfect qubits being repeatedly combined and manipulated into a magic state that is then put towards running an error-corrected operation, the magic state is “grown” within a smaller patch of qubits.

The Google researchers showed that magic state cultivation could, in theory, enable quantum computers to run consequential programs with just one-tenth the number of qubits we had expected they would need. The new experiment by Rosenfeld and her colleagues proves that magic state cultivation works in practice.

This is not the first time that new methods for making magic states have lowered a quantum computer’s error rates. Advances in creating magic states and error-correcting non-Clifford gates have also been achieved in . Willow’s qubits, which are based on tiny superconducting circuits, achieved error rates similar to those past experiments, but Rosenfeld says that they work more quickly, potentially reducing the quantum computer’s overall runtime.

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“This is an impressive demonstration of a promising recent idea pioneered by Google,” says at the University of California, Davis. “It’s an approach that can make fault-tolerant quantum computing substantially cheaper.” The new work demonstrates one of the building blocks necessary for universal quantum computing and could be a “gamechanger” in terms of building intermediately sized quantum computers with hundreds of logical qubits, says at ETH Zurich in Switzerland.

at Imperial College London says that while this experiment is a significant step forward, Willow’s errors will need to be reduced further before it can tackle truly useful problems. Additionally, Kim says that in the experiment, Willow generated good magic states only for a fraction of its computational runs: only 8 per cent of Willow’s computations produced high-quality magic states that could then be put into further use.

Rosenfeld says that this percentage ought to increase as she and her colleagues refine the technique and improve Willow’s hardware. It is, in this case, also affected by a very careful benchmarking technique that the team used, which may not be used in future applications, she says. “This is the first demonstration that the concepts work. We’re not done.”