Lifeboat Foundation

Unlike classical computers, which use bits to process information as either 0s or 1s, quantum computers use quantum bits, also known as qubits, which can represent and process both 0 and 1 simultaneously thanks to a quantum property called superposition. This fundamental difference gives quantum computers the potential to solve some complex problems much more efficiently than classical computers.

INL researcher Ernesto Galvão, in collaboration with Sapienza Università di Roma (Rome) and Istituto di Fotonica e Nanotecnologie (Milan), recently published a groundbreaking study in the journal Science Advances (“Polarization-encoded photonic quantum-to-quantum Bernoulli factory based on a quantum dot source”), where they describe a new set-up for a quantum-to-quantum Bernoulli factory.

A Bernoulli factory is a method to manipulate randomness, using as inputs random coin flips with a certain probability distribution, and outputting coin flips with a different, desired distribution.

Researchers at Purdue University have trapped alkali atoms (cesium) on an integrated photonic circuit, which behaves like a transistor for photons (the smallest energy unit of light) similar to electronic transistors. These trapped atoms demonstrate the potential to build a quantum network based on cold-atom integrated nanophotonic circuits. The team, led by Chen-Lung Hung, associate professor of physics and astronomy at the Purdue University College of Science, published their discovery in the American Physical Society’s Physical Review X (“Trapped Atoms and Superradiance on an Integrated Nanophotonic Microring Circuit”).

“We developed a technique to use lasers to cool and tightly trap atoms on an integrated nanophotonic circuit, where light propagates in a small photonic ‘wire’ or, more precisely, a waveguide that is more than 200 times thinner than a human hair,” explains Hung, who is also a member of the Purdue Quantum Science and Engineering Institute. “These atoms are ‘frozen’ to negative 459.67 degrees Fahrenheit or merely 0.00002 degrees above the absolute zero temperature and are essentially standing still. At this cold temperature, the atoms can be captured by a ‘tractor beam’ aimed at the photonic waveguide and are placed over it at a distance much shorter than the wavelength of light, around 300 nanometers or roughly the size of a virus. At this distance, the atoms can very efficiently interact with photons confined in the photonic waveguide. Using state-of-the-art nanofabrication instruments in the Birck Nanotechnology Center, we pattern the photonic waveguide in a circular shape at a diameter of around 30 microns (three times smaller than a human hair) to form a so-called microring resonator. Light would circulate within the microring resonator and interact with the trapped atoms.”

A key aspect function the team demonstrates in this research is that this atom-coupled microring resonator serves like a ‘transistor’ for photons. They can use these trapped atoms to gate the flow of light through the circuit. If the atoms are in the correct state, photons can transmit through the circuit. Photons are entirely blocked if the atoms are in another state. The stronger the atoms interact with the photons, the more efficient this gate is.

There is also chaotic computation.

The study “Chaotic neural dynamics facilitate probabilistic computations through sampling,” published in the Proceedings of the National Academy of Sciences (PNAS), explores how the brain’s inherent chaos aids in processing information.

RIKEN researchers have developed a model to explain how the brain computes probabilities using chaotic dynamics.

A mathematical model suggests there is an unusual region of space where objects can get pulled into the sun’s orbit – meaning we may have to redraw the boundary of the solar system.

By Jonathan O’Callaghan

Sam Altman, CEO of OpenAI,… said some kind of national payments would likely be needed as technology killed more jobs even as it generated massive wealth for others.

Many tech entrepreneurs have long suggested that guaranteed income could cushion job losses from AI and automation. The latest and largest study of the idea was spearheaded by the man behind ChatGPT.

AI models can easily generate essays and other types of text. However, they’re nowhere near as good at solving math problems, which tend to involve logical reasoning—something that’s beyond the capabilities of most current AI systems.

But that may finally be changing. Google DeepMind says it has trained two specialized AI systems to solve complex math problems involving advanced reasoning. The systems—called AlphaProof and AlphaGeometry 2—worked together to successfully solve four out of six problems from this year’s International Mathematical Olympiad (IMO), a prestigious competition for high school students. They won the equivalent of a silver medal.

In a public interview, Chinese biophysicist He Jiankui said he is receiving offers of financial support from figures in the US.

🧠🤖🩺

Research develops an automated system using machine learning to quantify motor symptoms in Parkinson’s disease and predict disease progression, offering new therapeutic insights.

Futurology: The global demand for AI computing has data centers consuming electricity like frat houses chug beer. But researchers from the University of Minnesota might have a wildly innovative solution to curb AI’s growing thirst for power with a radical new device that promises vastly superior energy efficiency.

The researchers have designed a new “computational random-access memory” (CRAM) prototype chip that could reduce energy needs for AI applications by a mind-boggling 1,000 times or more compared to current methods. In one simulation, the CRAM tech showed an incredible 2,500x energy savings.

Traditional computing relies on the decades-old von Neumann architecture of separate processor and memory units, which requires constantly moving data back and forth in an energy-intensive process. The Minnesota team’s CRAM completely upends that model by performing computations directly within the memory itself using spintronic devices called magnetic tunnel junctions (MTJs).