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Our spatial sense doesn’t extend beyond the familiar three dimensions, but that doesn’t stop scientists from playing with whatever lies beyond.

Rice University physicists are pushing spatial boundaries in new experiments. They’ve learned to control electrons in gigantic Rydberg atoms with such precision they can create “synthetic dimensions,” important tools for .

The Rice team developed a technique to engineer the Rydberg states of ultracold strontium atoms by applying resonant microwave electric fields to couple many states together. A Rydberg state occurs when one electron in the atom is energetically bumped up to a highly , supersizing its orbit to make the atom thousands of times larger than normal.

There are few places in the universe that invite as much curiosity—and terror—as the interior of a black hole. These extreme objects exert such a powerful gravitational pull that not even light can escape them, a feature that has left many properties of black holes unexplained.

Now, a team led by Enrico Rinaldi, a research scientist at the University of Michigan, have used quantum computing and deep learning to probe the bizarre innards of black holes under the framework of a mind-boggling idea called holographic duality. This idea posits that black holes, or even the universe itself, might be holograms.

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We construct quantum algorithms to compute physical observables of nonlinear PDEs with M initial data. Based on an exact mapping between nonlinear and linear PDEs using the level set method, these new quantum algorithms for nonlinear Hamilton-Jacobi and scalar hyperbolic PDEs can be performed with a computational cost that is independent of M, for arbitrary nonlinearity. Depending on the details of the initial data, it can also display up to exponential advantage in both the dimension of the PDE and the error in computing its observables. For general nonlinear PDEs, quantum advantage with respect to M is possible in the large M limit.

BEIJING, Feb. 17 (Xinhua) — China has released a new quantum computing programming software named “isQ-Core” and deployed it to the country’s superconducting quantum hardware platform.

It represents a significant step forward in the combination of home-grown quantum computing hardware and software, said its primary developer, the Institute of Software under the Chinese Academy of Sciences (CAS).

According to the institute, the isQ-Core has the advantages of simplicity, ease-of-use, high efficiency, solid scalability, and high reliability.

Historically, the first program you write for a new computer language is “Hello World,” or, if you are in Texas, “Howdy World.” But with quantum computing on the horizon, you need something better. Like “Hello Many Worlds.” [IonQ] proposes what that looks like and then writes it in seven different quantum languages in a post you should check out.

Here’s the description of the simple program:

The basic quantum program we’ll write is simple. It creates a fully-entangled state between two qubits, and then measures this state. This state is sometimes called a Bell State, or Bell Pair, after physicist John Stewart Bell.

In a paper published by Science, DeepMind demonstrates how neural networks can improve approximation of the Density Functional (a method used to describe electron interactions in chemical systems). This illustrates deep learning’s promise in accurately simulating matter at the quantum mechanical.


In a paper published in the scientific journal Science, DeepMind demonstrates how neural networks can be used to describe electron interactions in chemical systems more accurately than existing methods.

Density Functional Theory, established in the 1960s, describes the mapping between electron density and interaction energy. For more than 50 years, the exact nature of mapping between electron density and interaction energy — the so-called density functional — has remained unknown. In a significant advancement for the field, DeepMind has shown that neural networks can be used to build a more accurate map of the density and interaction between electrons than was previously attainable.

By expressing the functional as a neural network and incorporating exact properties into the training data, DeepMind was able to train the model to learn functionals free from two important systematic errors — the delocalization error and spin symmetry breaking — resulting in a better description of a broad class of chemical reactions.