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Scientists are a step closer to unraveling the mysterious forces of the universe after working out how to measure gravity on a microscopic level.

Experts have never fully understood how the force that was discovered by Isaac Newton works in the tiny quantum world. Even Einstein was baffled by quantum gravity and, in his theory of general relativity, said there is no realistic experiment that could show a quantum version of gravity.

But now physicists at the University of Southampton, working with scientists in Europe, have successfully detected a weak gravitational pull on a tiny particle using a new technique.

Watch some of the biggest names in physics debate the mysteries of the quantum and its future, including Roger Penrose, Sabine Hossenfelder, Avshalom Elitzur, Michio Kaku, Suchitra Sebastian, Priya Natarajan, Joscha Bach, Erik Verlinde, Hilary Lawson and Bjørn Ekeberg.

From string theory to quantum gravity and quantum computers, the quantum discourse is all the buzz in physics and beyond. But what is possible and what mere fantasy? Can we bring together relativity and quantum mechanics? Will we ever find a unified theory to explain our universe?

Continue reading “The quantum world: Dreams and delusions | Roger Penrose, Sabine Hossenfelder, Michio Kaku, and more!” »

A proposed recipe for quantum error correction removes the need for time-consuming measurements of qubits, replacing them with copying and feedback steps instead.

Researchers have characterized the thermodynamic properties of a model that uses cold atoms to simulate condensed-matter phenomena.

Scientists achieve breakthrough in quantum optics with photon detector-based method, paving the way for improved quantum computing.

Scientists at Paderborn University have used a new method to determine the characteristics of optical, i.e. light-based, quantum states. For the first time, they are using certain photon detectors — devices that can detect individual light particles — for so-called homodyne detection. The ability to characterize optical quantum states makes the method an essential tool for quantum information processing. Precise knowledge of the characteristics is important for use in quantum computers, for example. The results have now been published in the specialist journal Optica Quantum.

Advancements in Homodyne Detection.

It might sound like something from science fiction, but the holographic principle might help us answer the biggest problem in modern physics.

https://www.youtube.com/watch?v=tFx_UNW9I1U&si=QxOgeE59dOkGDFck

This definitely is a Lifeboat post embodying what Lifeboat is about, and it’s only about AI. They did a really good job explaining the 10 stages.

This video explores the 10 stages of AI, including God-Like AI. Watch this next video about the Technological Singularity: • Technological Singularity: 15 Ways It…
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Continue reading “The 10 Stages of Artificial Intelligence” »

For years, niobium was considered an underperformer when it came to superconducting qubits. Now, scientists supported by Q-NEXT have found a way to engineer a high-performing niobium-based qubit and take advantage of niobium’s superior qualities.

When it comes to quantum technology, niobium is making a comeback.

For the past 15 years, niobium has been sitting on the bench after experiencing a few mediocre at-bats as a core qubit material.

Networks can represent changing systems, like the spread of an epidemic or the growth of groups in a population of people. But the structure of these networks can change, too, as links appear or vanish over time. To better understand these changes, researchers often study a series of static “snapshots” that capture the structure of the network during a short duration.

Network theorists have sought ways to combine these snapshots. In a new paper in Physical Review Letters, a trio of SFI-affiliated researchers describe a novel way to aggregate static snapshots into smaller clusters of networks while still preserving the dynamic nature of the system. Their method, inspired by an idea from quantum mechanics, involves testing successive pairs of network snapshots to find those for which a combination would result in the smallest effect on the dynamics of the system—and then combining them.

Importantly, it can determine how to simplify the history of the network’s structure as much as possible while maintaining accuracy. The math behind the method is fairly simple, says lead author Andrea Allen, now a data scientist at Children’s Hospital of Philadelphia.