SQC produces nature’s perfect qubits: phosphorus atoms precision placed in pure silicon, the 14|15 qubit platform. We have the highest precision manufacturing practice on the planet, operating at 0.13 nanometer accuracy, in-house.
Fermilab and CERN unveil upgraded quantum sensors to enhance particle detection for future collider experiments.
The NA62 Collaboration has dramatically reduced the uncertainty in its measurement of an extremely rare particle decay, in results just presented at the 2026 La Thuile conference.
The study of rare decays gives physicists the chance to probe the Standard Model of particle physics. Researchers can determine what is known as the branching ratio of a decay, which describes how many particles decay through a particular process as a fraction of the total number of decays that occur.
The branching ratio of the decay that the NA62 Collaboration has studied—the decay of a positively charged kaon into a positively charged pion and neutrino–antineutrino pair (written K+→π+νν)—can be predicted theoretically with a very high degree of precision. Thanks to this “theoretical cleanliness,” this particular kaon decay is extremely sensitive to new physics beyond the Standard Model but, with a predicted branching ratio of less than one in 10 billion, it is extremely rare and very challenging to observe.
There is an art project to display every possible picture. The project admits this will take a long time, because there are many possible pictures. But how many? We will assume the very common color model known as True Color, in which each pixel can be one of 224 ≅ 17 million distinct colors. The digital camera shown below left has 12 million pixels. We’ll also consider much smaller pictures: the array below middle, with 300 pixels, and the array below right with just 12 pixels. Shown are some of the possible pictures:
12,000,000 pixels 300 pixels 12 pixels.
Quiz: Which of these produces a number of pictures similar to the number of atoms in the universe?
Answer: An array of n pixels produces (17 million)n different pictures. (17 million)12 ≅ 1,086, so the tiny 12-pixel array produces a million times more pictures than the number of atoms in the universe!
How about the 300 pixel array? It can produce 102,167 pictures. You may think the number of atoms in the universe is big, but that’s just peanuts to the number of pictures in a 300-pixel array. And 12M pixels? 1,086,696,638 pictures. Fuggedaboutit!
So the number of possible pictures is really, really, really big. And the number of atoms in the universe is looking relatively small, at least as a number of combinations.
On counting combinations People often underestimate the number of combinations of things. I think there are two main reasons: Combinations of things are multiplicative, while collections of things are additive. If you see a line of 6 people, it is easy to visualize a line of 60 people—it is ten times longer. But even if you know that there are 720 different orderings (permutations) in which those 6 people can line up, there is no way you can visualize the number of orderings for 60 people, because it is—you guessed it—larger than the number of atoms in the universe. Big numbers are hard. Even with simple collections of things, it takes practice to get a real intuition for the difference between 6 million and 6 billion people. When it comes to combinations, growth is faster and therefore intuition fails earlier. Authors are sloppy. Doug Smith reports that the New York Times confused “million” and “billion” over a dozen times per year; other sources also make similar mistakes. See the book by Unix co-creator Brian Kernighan for more on this. So beware, and be sure to use some simple math to augment your intuition when dealing with combinations.
NYU researchers have found a way to use light to control how microscopic particles assemble into crystals, effectively turning illumination into a tool for shaping matter. By adding light-sensitive molecules to a liquid filled with tiny particles, they can adjust how strongly the particles attract or repel one another simply by changing the light’s intensity or pattern. This allows them to trigger crystals to form, dissolve, or even be reshaped in real time.
Copper nanoparticles (Cu NPs) are effective catalysts for the electroreduction of CO2 (ECO2R) to multicarbon products but suffer from insufficient selectivity, aggregation, and deactivation. To address these challenges, we developed an in situ encapsulation strategy that engineers Cu NPs in a metal–organic framework (MOF) host from a simple one-pot hydrothermal synthesis, creating a selective and robust CO2R catalyst. The key design is the introduction of Sn additives during synthesis, which later evolve into single atoms (SAs) that serve a dual function: modulating the growth of Cu NPs from 3.35 to 9 nm and acting as active sites for the conversion of CO2 to CO. The locally generated CO then feeds adjacent Cu NPs, promoting subsequent C–C coupling via a tandem mechanism. The optimal catalyst, with a balanced Cu/Sn ratio, achieves a CO2-to-C2H4 Faradaic efficiency (FE) of 64%. Combined theoretical simulations and in situ infrared spectroscopy further reveal that Sn SAs promote Cu NPs electron transfer, enriching the electron density at active sites. This stabilizes *CO intermediates and reduces the energy barriers for CO2 activation and ensuing C–C coupling steps. This work presents a novel atomic- and nanoscale design strategy for advanced CO2RR catalysts.