Lifeboat Foundation

Detailed data on space-time ripples are set to pour in from LIGO and Virgo’s upgraded detectors.

IBM researchers found a method to reduce noise in quantum computing by amplifying noise at measurable intervals, and extrapolating a difference to calculate a “zero-noise” result.

The most popular contender over the past few decades has been string theory, and the related concepts of superstring theory and M-theory, in which particles are considered as tiny units of one-dimensional string. However, a lesser-known theory has also gained traction; loop quantum gravity (LQG), which attempts to solve the quantum gravity problem by focusing on the very fabric of spacetime, rather than the particles themselves.

In “Quantum Space,” the popular-science writer Jim Baggott lays out the basic principles of LQG for science enthusiasts. The book looks at how loop quantum gravity has emerged by following the work of two of its leading proponents, Carlo Rovelli and Lee Smolin, and assesses where the theory is now, and where it might be going.

Although the concepts are — not surprisingly — mind-boggling, Baggott asks deep questions about the nature of the universe, what space is actually composed of, and the existence of time itself. (The book covers a lot of challenging material, however, and some prior reading may help readers find their way.)

The Quantum Flagship was first announced in 2016, and on 29 October, the commission announced the first batch of fund recipients. The 20 international consortia, each of which includes public research institutions as well as industry, will receive a total of €132 million over 3 years for technology-demonstration projects.

One of the most ambitious EU ‘Flagship’ schemes yet has picked 20 projects, aiming to turn weird physics into useful products.

When a particle is completely isolated from its environment, the laws of quantum physics start to play a crucial role. One important requirement to see quantum effects is to remove all thermal energy from the particle motion, i.e. to cool it as close as possible to absolute zero temperature. Researchers at the University of Vienna, the Austrian Academy of Sciences and the Massachusetts Institute of Technology (MIT) are now one step closer to reaching this goal by demonstrating a new method for cooling levitated nanoparticles. They now publish their results in the renowned journal Physical Review Letters.

Tightly focused laser beams can act as optical “tweezers” to trap and manipulate tiny objects, from glass particles to living cells. The development of this method has earned Arthur Ashkin the last year’s Nobel prize in physics. While most experiments thus far have been carried out in air or liquid, there is an increasing interest for using optical tweezers to trap objects in ultra-high vacuum: such isolated particles not only exhibit unprecedented sensing performance, but can also be used to study fundamental processes of nanoscopic heat engines, or quantum phenomena involving large masses.

A key element in these research efforts is to obtain full control over the particle motion, ideally in a regime where the laws of quantum physics dominate its behavior. Previous attempts to achieve this, have either modulated the optical tweezer itself, or immersed the particle into additional light fields between highly reflecting mirror configurations, i.e. optical cavities.

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Artificial neural networks are the heart of machine learning algorithms and artificial intelligence. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt’s “perceptron”, but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a binary-valued perceptron, which shows exponential advantage in storage resources over alternative realizations. We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives answers consistent with the expected results. We show that this quantum model of a perceptron can be trained in a hybrid quantum-classical scheme employing a modified version of the perceptron update rule and used as an elementary nonlinear classifier of simple patterns, as a first step towards practical quantum neural networks efficiently implemented on near-term quantum processing hardware.

Lanthanum, diamond crushers and advanced computer models are changing the hunt for this extreme quantum mechanical effect.

Physicists have measured the sound of ‘nothingness’ at room temperature — an important step in our future ability to listen in to the Universe.

You can think of it a little like this — we’ve now been able to measure the way some of the ubiquitous ‘background noise’ of space interacts with our equipment, which will hopefully help us tune it out going forward.

After all, the entire Universe is crackling with the static of quantum physics, and in order to be able to pick up the faint echoes of distant astronomical giants — such as the gravitational waves rippling off a black hole merger, for example — we need to be able to tune out the quantum static.

Continue reading “Physicists Just Measured Quantum ‘Nothingness’ at Room Temperature” »

Experiment could test theory of quantum electrodynamics—if it ever can be done.