Lifeboat Foundation

Researchers at QuTech—a collaboration between the Delft University of Technology and TNO—have engineered a record number of six, silicon-based, spin qubits in a fully interoperable array. Importantly, the qubits can be operated with a low error-rate that is achieved with a new chip design, an automated calibration procedure, and new methods for qubit initialization and readout. These advances will contribute to a scalable quantum computer based on silicon. The results are published in Nature today.

Different materials can be used to produce qubits, the quantum analog to the bit of the classical computer, but no one knows which material will turn out to be best to build a large-scale quantum computer. To date there have only been smaller demonstrations of silicon quantum chips with high quality qubit operations. Now, researchers from QuTech, led by Prof. Lieven Vandersypen, have produced a six qubit chip in silicon that operates with low error-rates. This is a major step towards a fault-tolerant quantum computer using silicon.

To make the qubits, individual electrons are placed in a linear array of six “quantum dots” spaced 90 nanometers apart. The array of quantum dots is made in a silicon chip with structures that closely resemble the transistor—a common component in every computer chip. A quantum mechanical property called spin is used to define a qubit with its orientation defining the 0 or 1 logical state. The team used finely-tuned microwave radiation, magnetic fields, and electric potentials to control and measure the spin of individual electrons and make them interact with each other.

For billions of years, the Milky Way’s largest satellite galaxies—the Large and Small Magellanic Clouds—have followed a perilous journey. Orbiting one another as they are pulled in toward our home galaxy, they have begun to unravel, leaving behind trails of gaseous debris. And yet—to the puzzlement of astronomers—these dwarf galaxies remain intact, with ongoing vigorous star formation.

“A lot of people were struggling to explain how these streams of material could be there,” said Dhanesh Krishnarao, assistant professor at Colorado College. “If this gas was removed from these galaxies, how are they still forming stars?”

With the help of data from NASA’s Hubble Space Telescope and a retired satellite called the Far Ultraviolet Spectroscopic Explorer (FUSE), a team of astronomers led by Krishnarao has finally found the answer: the Magellanic system is surrounded by a corona, a protective shield of hot supercharged gas. This cocoons the two galaxies, preventing their gas supplies from being siphoned off by the Milky Way, and therefore allowing them to continue forming new stars.

Most mass in everyday matter around us resides in protons and neutrons inside the atomic nucleus. However, the lifetime of a free neutron—one not bounded to a nucleus—is unstable, decaying by a process called beta decay. For neutrons, beta decay involves the emission of a proton, an electron, and an anti-neutrino. Beta decay is a common process.

However, scientists have some significant uncertainties about the neutron lifetime and about the neutron decaying inside a nucleus that leads to a proton emission. This is called beta-delayed proton emission. There are only a few neutron-rich nuclei for which beta-delayed proton emission is energetically allowed. The radioactive nucleus beryllium-11 (¹¹ Be), an isotope that consists of 4 protons and 7 neutrons, with its last neutron very weakly bound, is among those rare cases. Scientists recently observed a surprising large beta-delayed proton decay rate for ¹¹ Be. Their work is published in Physical Review Letters.

The discovery of an exotic near-threshold resonance that favors proton decay is a key for explaining the beta-delayed proton decay of ¹¹ Be. The discovery is also a remarkable and not fully understood manifestation of quantum many-body physics. Many-body physics involves interacting subatomic particles. While scientists may know the physics that apply to each particle, the complete system can be too complex to understand.

A team of physicists has created a new way to self-assemble particles—an advance that offers new promise for building complex and innovative materials at the microscopic level.

Self-assembly, introduced in the early 2000s, gives scientists a means to “pre-program” particles, allowing for the building of materials without further human intervention—the microscopic equivalent of Ikea furniture that can assemble itself.

The breakthrough, reported in the journal Nature, centers on emulsions—droplets of oil immersed in water—and their use in the self-assembly of foldamers, which are unique shapes that can be theoretically predicted from the sequence of droplet interactions.

Have you ever been faced with a problem where you had to find an optimal solution out of many possible options, such as finding the quickest route to a certain place, considering both distance and traffic?

If so, the problem you were dealing with is what is formally known as a “combinatorial optimization problem.” While mathematically formulated, these problems are common in the real world and spring up across several fields, including logistics, network routing, machine learning, and materials science.

Continue reading “Scalable and fully coupled quantum-inspired processor solves optimization problems” »

Artificial Intelligence (AI), a term first coined at a Dartmouth workshop in 1956, has seen several boom and bust cycles over the last 66 years. Is the current boom different?

The most exciting advance in the field since 2017 has been the development of “Large Language Models,” giant neural networks trained on massive databases of text on the web. Still highly experimental, Large Language Models haven’t yet been deployed at scale in any consumer product — smart/voice assistants like Alexa, Siri, Cortana, or the Google Assistant are still based on earlier, more scripted approaches.

Continue reading “Blaise Aguera y Arcas and Melanie Mitchell: How Close Are We to AI?” »

A new field of science has been emerging at the intersection of neuroscience and high-performance computing — this is the takeaway from the 2022 BrainComp conference, which took place in Cetraro, Italy from the 19th to the 22nd of September. The meeting, which featured international experts in brain mapping, machine learning, simulation, research infrastructures, neuro-derived hardware, neuroethics and more, strengthened the current collaborations in this emerging field and forged new ones.

Now in its 5th edition, BrainComp first started in 2013 and is jointly organised by the Human Brain Project and the EBRAINS digital research infrastructure, University of Calabria in Italy, the Heinrich Heine University of Düsseldorf and the Forschungszentrum Jülich in Germany. It is attended by researchers from inside and outside the Human Brain Project. This year was dedicated to the computational challenges of brain connectivity. The brain is the most complex system in the observable universe due to the tight connections between areas down to the wiring of the individual neurons: decoding this complexity through neuroscientific and computing advances benefits both fields.

Hosted by the organising committee of Katrin Amunts, Scientific Research Director of the HBP, Thomas Lippert, Leader of EBRAINS Computing Services from the Juelich Supercomputing Centre and Lucio Grandinetti from the University of Calabria, the sessions included a variety of topics over four days.

Webb’s Mid-Infrared Instrument reveals a network of scaffolding behind galaxy IC 5332’s iconic spiral structure.

DART demonstrated using a kinetic impactor to crash into a small asteroid and alter its orbital path.