Lifeboat Foundation

Several thousand sensors distributed over a square kilometer near the South Pole are tasked with answering one of the large outstanding questions in physics: does quantum gravity exist? The sensors monitor neutrinos—particles with no electrical charge and almost without mass—arriving at the Earth from outer space. A team from the Niels Bohr Institute (NBI), University of Copenhagen, has contributed to developing the method that exploits neutrino data to reveal if quantum gravity exists.

If as we believe, quantum gravity does indeed exist, this will contribute to unite the current two worlds in physics. Today, classical physics describes the phenomena in our normal surroundings such as gravity, while the atomic world can only be described using quantum mechanics.

The unification of quantum theory and gravitation remains one of the most outstanding challenges in fundamental physics. It would be very satisfying if we could contribute to that end, says Tom Stuttard, Assistant Professor at NBI.

Scientists have advanced the field by stabilizing exciton-polaritons in semiconductor photonic gratings, achieving long-lived and optically configurable quantum fluids suitable for complex system simulations.

Researchers from CNR Nanotec in Lecce and the Faculty of Physics at the University of Warsaw used a new generation of semiconductor photonic gratings to optically tailor complexes of quantum droplets of light that became bound together into macroscopic coherent states. The research underpins a new method to simulate and explore interactions between artificial atoms in a highly reconfigurable manner, using optics. The results have been published in the prestigious journal Nature Physics.

Quantum Simulation Technologies

Researchers have developed methods to entangle large numbers of particles, improving the precision and speed of quantum measurements. These advancements could revolutionize quantum sensors and atomic clocks, with potential applications in fundamental physics research.

Opening new possibilities for quantum sensors, atomic clocks, and tests of fundamental physics, JILA researchers have developed new ways of “entangling” or interlinking the properties of large numbers of particles. In the process they have devised ways to measure large groups of atoms more accurately even in disruptive, noisy environments.

The new techniques are described in a pair of papers published in Nature.^[1] JILA is a joint institute of the National Institute of Standards and Technology (NIST) and the University of Colorado Boulder.

Using observations made with an array of thousands of particle detectors, researchers have uncovered an important clue about cosmic rays that originate from outside of our Galaxy.

In the search for the most scalable hardware to use for quantum computers, qubits made of individual atoms are having a breakout moment.

Carbon nanotubes are cylindrical molecules that consist of rolled-up sheets of single-layer carbon atoms (graphene); they possess unique properties like high aspect ratio, mechanical strength, electrical and thermal conductivity, chemical stability, and a tip-surface area near the theoretical limit. They are one of the strongest materials known to man.

Solar eruptions are sending a stream of particles towards Earth, creating spectacular auroras in both hemispheres.

The aurora borealis – in the northern hemisphere – will be potentially visible on Monday night in the US as far south as the midwest. The northern lights, more commonly seen within the Arctic Circle, could also be visible in Scotland.

Princeton researchers have trained an AI to predict and prevent a common problem arising during nuclear fusion reactions — and they think it might be able to solve other problems, too.

The challenge: If the Spice Girls were physicists, their song “2 Become 1” might have been about nuclear fusion, a reaction that occurs when two atoms merge.

Fusion releases a tremendous amount of energy in the form of heat — it’s what powers the sun and other stars — and if we could harness the reaction here on Earth, we would have a near limitless source of clean energy.

Bayesian neural networks (BNNs) combine the generalizability of deep neural networks (DNNs) with a rigorous quantification of predictive uncertainty, which mitigates overfitting and makes them valuable for high-reliability or safety-critical applications. However, the probabilistic nature of BNNs makes them more computationally intensive on digital hardware and so far, less directly amenable to acceleration by analog in-memory computing as compared to DNNs. This work exploits a novel spintronic bit cell that efficiently and compactly implements Gaussian-distributed BNN values. Specifically, the bit cell combines a tunable stochastic magnetic tunnel junction (MTJ) encoding the trained standard deviation and a multi-bit domain-wall MTJ device independently encoding the trained mean. The two devices can be integrated within the same array, enabling highly efficient, fully analog, probabilistic matrix-vector multiplications. We use micromagnetics simulations as the basis of a system-level model of the spintronic BNN accelerator, demonstrating that our design yields accurate, well-calibrated uncertainty estimates for both classification and regression problems and matches software BNN performance. This result paves the way to spintronic in-memory computing systems implementing trusted neural networks at a modest energy budget.

The powerful ability of deep neural networks (DNNs) to generalize has driven their wide proliferation in the last decade to many applications. However, particularly in applications where the cost of a wrong prediction is high, there is a strong desire for algorithms that can reliably quantify the confidence in their predictions (Jiang et al., 2018). Bayesian neural networks (BNNs) can provide the generalizability of DNNs, while also enabling rigorous uncertainty estimates by encoding their parameters as probability distributions learned through Bayes’ theorem such that predictions sample trained distributions (MacKay, 1992). Probabilistic weights can also be viewed as an efficient form of model ensembling, reducing overfitting (Jospin et al., 2022). In spite of this, the probabilistic nature of BNNs makes them slower and more power-intensive to deploy in conventional hardware, due to the large number of random number generation operations required (Cai et al., 2018a).