Category Physics

Quantum computers could be made with fewer overall components, thanks to technology inspired by Schrödinger’s cat. A team of researchers from Amazon Web Services has used “bosonic cat qubits,” to improve the ability of quantum computers to correct errors. The demonstration of quantum error correction requiring reduced hardware overheads is reported in a paper published in Nature.

The system uses so-called cat qubits (qubits are the quantum equivalent to classical computing bits), which are designed to be resistant against certain types of noise and errors that might disrupt the output of quantum systems. This approach requires fewer overall components to achieve quantum error correction than other designs.

Quantum computers are prone to errors, ...

A team of Carnegie Mellon University researchers set out to see how accurately large language models (LLMs) can match the style of text written by humans. Their findings were recently published in the Proceedings of the National Academy of Sciences.

“We humans, we adapt how we write and how we speak to the situation. Sometimes we’re formal or informal, or there are different styles for different contexts,” said Alex Reinhart, lead author and associate teaching professor in the Department of Statistics & Data Science.

“What we learned is that LLMs, like ChatGPT and Llama, write a certain way, and they don’t necessarily adapt to the writing style...

Quantum light sources are fickle. They can flicker like stars in the night sky and can fade out like a dying flashlight. However, newly published research from the University of Oklahoma proves that adding a covering to one of these light sources, called a colloidal quantum dot, can cause them to shine without faltering, opening the door to new, affordable quantum possibilities. The findings are available in Nature Communications.

Quantum dots, or QDs, are so small that if you scaled up a single quantum dot to the size of a baseball, a baseball would be the size of the moon...

Artificial Intelligence (AI), particularly large language models like GPT-4, has shown impressive performance on reasoning tasks. But does AI truly understand abstract concepts, or is it just mimicking patterns? A new study from the University of Amsterdam and the Santa Fe Institute reveals that while GPT models perform well on some analogy tasks, they fall short when the problems are altered, highlighting key weaknesses in AI’s reasoning capabilities. The work is published in Transactions on Machine Learning Research.

Analogical reasoning is the ability to draw a comparison between two different things based on their similarities in certain aspects...