Lifeboat Foundation

Precisely measuring the energy states of individual atoms has been a historical challenge for physicists due to atomic recoil. When an atom interacts with a photon, the atom “recoils” in the opposite direction, making it difficult to measure the position and momentum of the atom precisely. This recoil can have big implications for quantum sensing, which detects minute changes in parameters, for example, using changes in gravitational waves to determine the shape of the Earth or even detect dark matter.

MIT physicists have developed a technique that allows them to arrange atoms in much closer proximity, down to a mere 50 nanometers.

Proximity is key for many quantum phenomena, as interactions between atoms are stronger when the particles are close. In many quantum simulators, scientists arrange atoms as close together as possible to explore exotic states of matter and build new quantum materials.

The open CMS detector during the second long shutdown of CERN’s accelerator complex. (Image: CERN) When we look at ourselves in a mirror, we see a virtual twin, identical in every detail except with left and right inverted. In particle physics, a transformation in which charge–parity (CP) symmetry is respected swaps a particle with the mirror image of its antimatter particle, which has opposite properties such as electric charge. The physical laws that govern nature don’t respect CP symmetry, however. If they did, the Universe would contain equal amounts of matter and antimatter, as it is believed to have done just after the Big Bang. To explain the large imbalance between matter and antimatter seen in the present-day Universe, CP symmetry has to be violated to a great extent. The Standard Model of particle physics can account for some CP violation, but it is not sufficient to explain the present-day matter–antimatter imbalance, prompting researchers to explore CP violation in all its known and unknown manifestations. One way CP violation can manifest itself is in the “mixing” of electrically neutral mesons such as the strange beauty meson, which is composed of a strange quark and a bottom antiquark. These mesons can travel macroscopic distances in the Large Hadron Collider (LHC) detectors before decaying into lighter particles, and during this journey they can turn into their corresponding antimesons and back. This phenomenon, called meson mixing, could be different for a meson turning into an antimeson versus an antimeson turning into a meson, generating CP violation. To see if that’s the case, researchers need to count how many mesons or antimesons survive a certain duration before decaying, and then repeat the measurement for a given range of durations. To do so, they have to separate mesons from antimesons, a task called flavour tagging. This task is crucial to pinning down CP violation in meson mixing and in the interference between meson mixing and decay. At a seminar held recently at CERN, the CMS collaboration at the LHC reported the first evidence of CP violation in the decay of the strange beauty meson into a pair of muons and a pair of electrically charged kaons. By deploying a new flavour-tagging algorithm on a sample of about 500 000 decays of the strange beauty meson into a pair of muons and a pair of charged kaons, collected during Run 2 of the LHC, the CMS collaboration measured with improved precision the parameter that determines CP violation in the interference between this meson’s mixing and decay. If this parameter is zero, CP symmetry is respected. The new flavour-tagging algorithm is based on a cutting-edge artificial intelligence (AI) technique called a graph neural network, which performs accurate flavour tagging by gathering information from the particles surrounding the strange beauty meson and those being produced alongside it. The collaboration then combined the result with its previous measurement of the parameter based on data from Run 1 of the LHC. The combined result is different from zero and is consistent with the Standard Model prediction and with previous measurements from CMS and the ATLAS and LHCb experiments. Notably, the combined result is comparable in precision to the world’s most precise measurement of the parameter, obtained by LHCb, a detector specifically designed to perform measurements of this kind. Moreover, the result has a statistical significance that crosses the conventional “3 sigma” threshold, providing the first evidence of CP violation in the decay of the strange beauty meson into a pair of muons and a pair of charged kaons. The result marks a milestone in CMS’s studies of CP violation. Thanks to AI, CMS has pushed the boundary of what its detector can achieve in the exploration of this fundamental matter–antimatter asymmetry. Find out more on the CMS website.

Scientists inside and outside of particle physics and astrophysics are leaning on AI for assistance with complex tasks.

A new imaging technique, which captured frozen lithium atoms transforming into quantum waves, could be used to probe some of the most poorly understood aspects of the quantum world.

The theory was developed by Neils Bohr’s great-grandson.

Quantum mechanics, the most potent theory physicists have developed, doesn’t make sense. What I mean by that statement is that quantum mechanics — which was developed to describe the microworld of molecules, atoms, and subatomic particles — leaves its users without a common-sense picture of what it describes. Full of what seem to be paradoxes and puzzles, quantum physics demands, for most scientists, an interpretation: a way of making sense of its mathematical formalism in terms of a concrete description of what exists in the world and how we interact with it. Unfortunately, after a century not one but a basketful of “quantum interpretations” have been proposed. Which one is correct? Which one most clearly understands what quantum physics has been trying to tell us these past 100 years?

In light of these questions, I’m beginning a series that explores the most radical of all the quantum interpretations, the one I think gets it right, or at least is pointed in the right direction. It is a relative newcomer to the scene, so you may not have heard of it. But it has been gaining a lot of attention recently because it doesn’t just ask us to reimagine how we view the science of atoms; it asks us to reimagine the process of science itself.

The term “QBism” was shorthand for “Quantum Bayesianism” when this idea/theory/interpretation was first proposed in the late 1990s and early 2000s. The name hit the nail on the head because “Bayesianism” is a radical way of interpreting probabilities. The Bayesianist approach to what we mean by probability differs strongly from what you learned in school about coin flips and dice rolls and how frequently a particular result can be expected to appear. Since probabilities lie at the heart of quantum mechanics, QBism zeroed in on a key aspect of quantum formalism — one that other interpretations had missed or swept under the rug — because it focused squarely on how we interpret probabilities. We’re going to dig deep into all of this as we go along in this series, but since today’s column is supposed to be the introduction, let’s start with a 10,000-foot view of what’s at stake in the great “Quantum Interpretation Wars” so we can see where QBism fits in.

Now Princeton researchers have sparked new life into static. Using millions of hours of computational time to run detailed simulations, the researchers found a way to describe static charge atom-by-atom with the mathematics of heat and work. Their paper appeared in Nature Communications on March 23.

The study looked specifically at how charge moves between materials that do not allow the free flow of electrons, called insulating materials, such as vinyl and acrylic. The researchers said there is no established view on what mechanisms drive these jolts, despite the ubiquity of static: the crackle and pop of clothes pulled from a dryer, packing peanuts that cling to a box.

“We know it’s not electrons,” said Mike Webb, assistant professor of chemical and biological engineering, who led the study. “What is it?”

Theoretical physicists employ their imaginations and their deep understanding of mathematics to decipher the underlying laws of the universe that govern particles, forces and everything in between. More and more often, theorists are doing that work with the help of machine learning.

As might be expected, the group of theorists using machine learning includes people classified as “computational” theorists. But it also includes “formal” theorists, the people interested in the self-consistency of theoretical frameworks, like string theory or quantum gravity. And it includes “phenomenologists,” the theorists who sit next to experimentalists, hypothesizing about new particles or interactions that could be tested by experiments; analyzing the data the experiments collect; and using results to construct new models and dream up how to test them experimentally.

In all areas of theory, machine-learning algorithms are speeding up processes, performing previously impossible calculations, and even causing theorists to rethink the way theoretical physics research is done.