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.

The use of artificial intelligence (AI) in writing and editing emails has become a common practice among professionals, with over 75% of them utilizing tools like ChatGPT, Gemini, Copilot, or Claude in their daily work. While these generative AI tools can make writing easier, research reveals that relying on them too heavily can undermine trust between managers and employees.

A study conducted by researchers Anthony Coman and Peter Cardon surveyed 1,100 professionals about their perceptions of emails written with low, medium, and high levels of AI assistance. The results showed a “perception gap” in messages written by managers versus those written by employees. When evaluating their own use of AI, participants tended to rate it similarly across different levels of assistance. However, when rating others’ use, the magnitude of AI assistance became important.

The study found that low levels of AI help, such as grammar or editing, were generally acceptable. However, higher levels of assistance triggered negative perceptions, especially among employees who perceived their managers’ reliance on AI-generated content as laziness or a lack of caring. This perception gap had a substantial impact on trust: only 40% to 52% of employees viewed supervisors as sincere when they used high levels of AI, compared to 83% for low-assistance messages.

The findings suggest that managers should carefully consider message type, level of AI assistance, and relational context before using AI in their writing. While AI may be suitable for informational or routine communications, relationship-oriented messages requiring empathy, praise, congratulations, motivation, or personal feedback are better handled with minimal technological intervention.

In essence, the quiet threat to trust posed by overreliance on AI emails is a reminder that while technology can enhance productivity and efficiency, it cannot replace human touch and emotional intelligence in workplace relationships.

The article you provided showcases a groundbreaking achievement in materials discovery research. A team of scientists has developed an AI-powered laboratory that can collect at least 10 times more data than previous techniques, drastically expediting the process while slashing costs and environmental impact. This self-driving laboratory combines machine learning and automation with chemical and materials sciences to discover materials more quickly.

The innovation lies in the implementation of dynamic flow experiments, where chemical mixtures are continuously varied through the system and monitored in real-time. This approach generates a vast amount of high-quality data, which is then used by the machine-learning algorithm to make smarter, faster decisions, honing in on optimal materials and processes.

The results are staggering: the self-driving lab can identify the best material candidates on its very first try after training, reducing the number of experiments needed and dramatically cutting down on chemical use and waste. This breakthrough has far-reaching implications for sustainable research practices and society’s toughest challenges.

The article highlights the work of Milad Abolhasani, corresponding author of the paper, who emphasizes that this achievement is not just about speed but also about responsible research practices. The future of materials discovery, he says, is not just about how fast we can go, but also about how responsibly we get there.

The paper, “Flow-Driven Data Intensification to Accelerate Autonomous Materials Discovery,” was published in the journal Nature Chemical Engineering and showcases a collaborative effort from multiple researchers and institutions. The work has been supported by the National Science Foundation and the University of North Carolina Research Opportunities Initiative program.