Curious Kids is a series for children of all ages. If you have a question you’d like an expert to answer, send it to [email protected].
How do we know the age of the planets and stars? – Swara D., age 13, Thane, India
Measuring the ages of planets and stars helps scientists understand when they formed and how they change – and, in the case of planets, if life has had time to have evolved on them.
Researchers at the University of Hong Kong (HKU) have designed an innovative pixelated, soft, color-changing system called a Morphable Concavity Array (MoCA).
Pixelated, soft, color-changing systems are malleable structures that can change color by manipulating light. They have applications in a wide range of industries, from medical bandages that change color if there is an infection, to foldable screens on smartphones and tablets, as well as wearable technology where sensors are integrated into the clothing fabric.
The research was co-directed by Professor Anderson Ho Cheung Shum from the Department of Mechanical Engineering at HKU, and Professor Mingzhu Li from the Institute of Chemistry, Chinese Academy of Sciences, and led by Dr. Yi Pan from the Department of Mechanical Engineering at HKU.
Ben Dixon, a researcher in the Optical and Quantum Communications Technology Group, explains how the process works: “First, you need to generate pairs of specific entangled qubits (called Bell states) and transmit them in different directions across the network link to two separate quantum repeaters, which capture and store these qubits. One of the quantum repeaters then does a two-qubit measurement between the transmitted and stored qubit and an arbitrary qubit that we want to send across the link in order to interconnect the remote quantum systems. The measurement results are communicated to the quantum repeater at the other end of the link; the repeater uses these results to turn the stored Bell state qubit into the arbitrary qubit. Lastly, the repeater can send the arbitrary qubit into the quantum system, thereby linking the two remote quantum systems.”
To retain the entangled states, the quantum repeater needs a way to store them — in essence, a memory. In 2020, collaborators at Harvard University demonstrated holding a qubit in a single silicon atom (trapped between two empty spaces left behind by removing two carbon atoms) in diamond. This silicon “vacancy” center in diamond is an attractive quantum memory option. Like other individual electrons, the outermost (valence) electron on the silicon atom can point either up or down, similar to a bar magnet with north and south poles. The direction that the electron points is known as its spin, and the two possible spin states, spin up or spin down, are akin to the ones and zeros used by computers to represent, process, and store information. Moreover, silicon’s valence electron can be manipulated with visible light to transfer and store a photonic qubit in the electron spin state. The Harvard researchers did exactly this; they patterned an optical waveguide (a structure that guides light in a desired direction) surrounded by a nanophotonic optical cavity to have a photon strongly interact with the silicon atom and impart its quantum state onto that atom. Collaborators at MIT then showed this basic functionality could work with multiple waveguides; they patterned eight waveguides and successfully generated silicon vacancies inside them all.
Lincoln Laboratory has since been applying quantum engineering to create a quantum memory module equipped with additional capabilities to operate as a quantum repeater. This engineering effort includes on-site custom diamond growth (with the Quantum Information and Integrated Nanosystems Group); the development of a scalable silicon-nanophotonics interposer (a chip that merges photonic and electronic functionalities) to control the silicon-vacancy qubit; and integration and packaging of the components into a system that can be cooled to the cryogenic temperatures needed for long-term memory storage. The current system has two memory modules, each capable of holding eight optical qubits.
Of all the holy grails in robotics, learning may well be the holiest. In an era when the term “general purpose” is tossed around with great abandon, however, it can be difficult for non-roboticists to understand what today’s systems can — and can’t — do. The truth of it is that most robots these days are built to do one (or a couple, if you’re lucky) thing really well.
It’s a truth that spans the industry, from the lowliest robot vacuum to the most advanced industrial system. So, how do we make the transition from single to general purpose robotics? Certainly, there are going to be a lot of stops in multipurpose land along the way.
The answer is, of course, robot learning. Walk into nearly any robotics research lab these days and you will find teams working on tackling the issue. The same applies to startups and corporations, as well. Look at companies Viam and Intrinsic, which are working to lower the bar of entry for robot programming.
The latest research on quantum materials and electron curves could revamp our energy-efficient electronics.
The Renaissance sculptor Michelangelo was known for claiming that he deserved little credit for his beautiful works: they were already there inside the rock, he merely cut them out. ‘Every block of stone,’ he said, ‘has a statue inside it and it is the task of the sculptor to discover it. I saw the angel in the marble and carved until I set him free.’
The final product already existed within Michelangelo’s ideals. But it took years of trial and error, practice, and failure to reach the point of being able to give form to it. In a similar sense, Nietzsche would say the ‘you’ that you must become is already there. It’s already inscribed in your values. That which you admire – the preponderance of all your latent virtues – reflects who you are in the truest sense.
The act of becoming who you are is the act of carving your ideal self out of the hard stone of your psyche – of bringing greater and greater refinement to the crude shapes of character that exist in you now. Simultaneously an act of discovery and creation, to become who you are is to bring your virtues to life and synthesise them into a unified whole. Nietzsche proclaims:
Without full fault tolerance in quantum computers we will never practically get past 100 qubits but full fault tolerance will eventually open up the possibility of billions of qubits and beyond. In a Wright Brothers Kittyhawk moment for Quantum Computing, a fully fault-tolerant algorithm was executed on real qubits. They were only three qubits but this was never done on real qubits before.
This is the start of the fully fault tolerant age of quantum computers. For quantum computers to be the real deal of unlimited computing disruption then we needed full fault tolerance on real qubits.
Scientists have made a significant breakthrough in understanding and overcoming the challenges associated with Ni-rich cathode materials used in lithium-ion batteries.
While these materials can reach high voltages and capacities, their real-world usage has been limited by structural issues and oxygen depletion.
Their study revealed that ‘oxygen hole’ formation – where an oxygen ion loses an electron — plays a crucial role in the degradation of LiNiO2 cathodes accelerating the release of oxygen which can then further degrade the cathode material.
Combining two cutting-edge technologies, researchers revealed the impact of a multitude of genes that are associated with neurodevelopmental disorders, including autism, but whose effects on human brain development were previously unknown.
The image generator inside the AI-powered Bing Chat is getting a big upgrade today: Microsoft announced that OpenAI’s latest DALL-E 3 model is now available to all Bing Chat and Bing Image Creator users. It has been rolling out over the last week or so, first to Bing Enterprise users and then to Bing Image Creator, but now it’s open to everyone.
Bing is getting DALL-E 3 access even before OpenAI’s own ChatGPT does — that’s scheduled to happen this month, but only for paying users. Microsoft is likely to be the most popular image generating tool for a while.
“Microsoft is planning to use DALL-E tech in more than just Bing, too. It’s working on an AI image creation tool in the Paint app called Paint Cocreator, for instance, which will bring the DALL-E model right into Windows.”
OpenAI’s latest image creation tool is supposed to be more creative and more realistic — and it’ll finally understand your prompt.
