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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.

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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.

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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.

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Scaling up qubit counts in quantum computers is at the core of achieving quantum supremacy.

Among the troublesome hurdles of this scaling-up race is refining how qubits are measured. Devices called parametric amplifiers are traditionally used to do these measurements. But as the name suggests, the device amplifies weak signals picked up from the qubits to conduct the readout, which causes unwanted noise and can lead to decoherence of the qubits if not protected by additional large components. More importantly, the bulky size of the amplification chain becomes technically challenging to work around as qubit counts increase in size-limited refrigerators.

Cue the Aalto University research group Quantum Computing and Devices (QCD). They have a hefty track record of showing how thermal bolometers can be used as ultrasensitive detectors, and they just demonstrated in an April 10 Nature Electronics paper that bolometer measurements can be accurate enough for single-shot qubit readout.

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