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JILA physicists have measured Albert Einstein’s theory of general relativity, or more specifically, the effect called time dilation, at the smallest scale ever, showing that two tiny atomic clocks, separated by just a millimeter or the width of a sharp pencil tip, tick at different rates.

The experiments, described in the February 17, 2022, issue of Nature, suggest how to make atomic clocks 50 times more precise than today’s best designs and offer a route to perhaps revealing how relativity and gravity interact with quantum mechanics, a major quandary in physics.

JILA is jointly operated by the National Institute of Standards and Technology (NIST) and the University of Colorado Boulder.

The world’s most precise atomic clock has confirmed that the time dilation predicted by Albert Einstein’s theory of general relativity works on the scale of millimetres.

Physicists have been unable to unite quantum mechanics – a theory that describes matter at the smallest scales – with general relativity, which predicts the behaviour of objects at the largest cosmic scales, including how gravity bends space-time. Because gravity is weak over small distances, it is hard to measure relativity on small scales.

But atomic clocks, which count seconds by measuring the frequency of radiation emitted when electrons around an atom change energy states, can detect these minute gravitational effects.

A light in the dark — If quantum computers continue to advance, and perform more calculations for less steep costs, Rinaldi and his team might be able to reveal what happens inside of black holes, beyond the event horizon — a region immediately surrounding a black hole’s singularity, within which not even light, nor perhaps time itself, can escape the immense force of gravity.

In practical terms, the event horizon prevents all conventional, light-based observations. But, and perhaps more compelling, the team hopes that further advances in this line of inquiry will do more than peek inside a black hole, and unlock what physicists have dreamed of since the days of Einstein: a unified theory of physics.

You’re not bad at math. Like pearls before swine, your beautiful brain is just far too complex for such basic things. property= description.

Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning. Now they can reduce quantum computing errors using custom machine learning algorithms.

ETH physicists have modified one of the major schemes for quantum error correction and put it into practice, demonstrating that they can substantially prolong the lifetime of quantum states—a crucial ingredient for future large-scale quantum computers.

In modern computing devices, literally billions of transistors work restlessly in almost perfect harmony. The keys to producing near-perfect computation from devices made from imperfect components are the use of digitisation and error correction, with the latter encompassing procedures to detect and rectify inaccuracies as they occur. The challenge of preventing errors from accumulating is one that future quantum computers have to face as well—in fact it forms the main barrier to realizing useful computations. Alas, the tools that have been perfected for classical computers cannot be applied directly to quantum computers, which play by another set of rules, those of quantum mechanics. Ingenious solutions for quantum error correction have been proposed over the past couple of decades, and recently there has been encouraging progress towards implementing such methods in state-of-the-art quantum computers. Writing in Nature Physics, the group of Prof.

The thing is, it could be—and a University of Michigan physicist is using quantum computing and machine learning to better understand the idea, called holographic duality.

Similarly, entanglement seems to be fundamental to the existence of space-time. This was the conclusion reached by a pair of postdocs in 2006: Shinsei Ryu (now at the University of Illinois, Urbana-Champaign) and Tadashi Takayanagi (now at Kyoto University), who shared the 2015 New Horizons in Physics prize for this work. “The idea was that the way that [the geometry of] space-time is encoded has a lot to do with how the different parts of this memory chip are entangled with each other,” Van Raamsdonk explained.

Inspired by their work, as well as by a subsequent paper of Maldacena’s, in 2010 Van Raamsdonk proposed a thought experiment to demonstrate the critical role of entanglement in the formation of space-time, pondering what would happen if one cut the memory chip in two and then removed the entanglement between qubits in opposite halves. He found that space-time begins to tear itself apart, in much the same way that stretching a wad of gum by both ends yields a pinched-looking point in the center as the two halves move farther apart. Continuing to split that memory chip into smaller and smaller pieces unravels space-time until only tiny individual fragments remain that have no connection to one another. “If you take away the entanglement, your space-time just falls apart,” said Van Raamsdonk. Similarly, “if you wanted to build up a space-time, you’d want to start entangling [qubits] together in particular ways.”

Combine those insights with Swingle’s work connecting the entangled structure of space-time and the holographic principle to tensor networks, and another crucial piece of the puzzle snaps into place. Curved space-times emerge quite naturally from entanglement in tensor networks via holography. “Space-time is a geometrical representation of this quantum information,” said Van Raamsdonk.

The potential of quantum computers to solve problems that are intractable for classical computers has driven advances in hardware fabrication. In practice, the main challenge in realizing quantum computers is that general, many-particle quantum states are highly sensitive to noise, which inevitably causes errors in quantum algorithms. Some noise sources are inherent to the current materials platforms. de Leon et al. review some of the materials challenges for five platforms for quantum computers and propose directions for their solution.

Science, this issue p. eabb2823.