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Silicon Photonics Manufacturing Ramps Up

The promise of photonics ICs is spurring innovation, but complex processes and a lack of open foundries are keeping it from reaching its full potential.

Circuit scaling is starting to hit a wall as the laws of physics clash with exponential increases in the volume of data, forcing chipmakers to take a much closer look at silicon photonics as a way of moving data from where it is collected to where it is processed and stored.

The laws of physics are immutable. Put simply, there are limits to how fast an electron can travel through copper. The speed of an electron, while fast on a macroscopic scale, encounters significant resistance as pathways shrink, leading to heat generation and power inefficiencies. In contrast, silicon photonics circumvents these electrical limitations by harnessing the swiftness of photons, which travel at the speed of light and are not bound by the resistive properties of materials like copper. Unlike electrons, photons do not generate significant heat, can carry more data due to their higher frequency, and suffer from less signal degradation.

Bayesian Machine Learning

Have you ever wondered how machine learning systems can improve their predictions over time, seemingly getting smarter with each new piece of data? This is not just a trait of all machine learning models but is particularly pronounced in Bayesian Machine Learning (BML), which stands apart for its ability to incorporate prior knowledge and uncertainty into its learning process. This article takes you on a deep dive into the world of BML, unraveling its concepts and methodologies, and showcasing its unique advantages, especially in scenarios where data is scarce or noisy.

Note that Bayesian Machine Learning goes hand-in-hand with the concept of Probabilistic Models. To discover more about Probabilistic Models in Machine Learning, click here.

Bayesian Machine Learning (BML) represents a sophisticated paradigm in the field of artificial intelligence, one that marries the power of statistical inference with machine learning. Unlike traditional machine learning, which primarily focuses on predictions, BML introduces the concept of probability and inference, offering a framework where learning evolves with the accumulation of evidence.

Evolutionary biologists show that the color variants of female cuckoos are based on ancient mutations

Every cuckoo is an adopted child—raised by foster parents, into whose nest the cuckoo mother smuggled her egg. The cuckoo mother is aided in this subterfuge by her resemblance to a bird of prey. There are two variants of female cuckoos: a gray morph that looks like a sparrowhawk, and a rufous morph. Male cuckoos are always gray.

“With this mimicry, the bird imitates dangerous predators of the birds, so that they keep their distance instead of attacking,” says Professor Jochen Wolf from LMU Munich.

Together with researchers at CIBIO (Centro de Investigação em Biodiversidade e Recursos Genéticos, Portugal), the evolutionary biologist has investigated the genetic foundations of the variant coloring, which is limited to females and emerged over the long evolutionary arms race between host and . The research is published in the journal Science Advances.

Compact Quantum Light Processing: Time-Bending Optical Computing Breakthrough

An international collaboration of researchers, led by Philip Walther at University of Vienna, have achieved a significant breakthrough in quantum technology, with the successful demonstration of quantum interference among several single photons using a novel resource-efficient platform. The work published in the prestigious journal Science Advances represents a notable advancement in optical quantum computing that paves the way for more scalable quantum technologies.

Interference among photons, a fundamental phenomenon in quantum optics, serves as a cornerstone of optical quantum computing. It involves harnessing the properties of light, such as its wave-particle duality, to induce interference patterns, enabling the encoding and processing of quantum information.

In traditional multi-photon experiments, spatial encoding is commonly employed, wherein photons are manipulated in different spatial paths to induce interference. These experiments require intricate setups with numerous components, making them resource-intensive and challenging to scale.