Category: quantum physics – Page 658
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Quantum algorithms for computing observables of nonlinear partial differential equations
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.
China releases new quantum computing software
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.
Hello (Many Quantum) World(s)
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.
DeepMind Simulates Matter on the Nanoscale With Artificial Intelligence
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.
Is The Wave Function The Building Block of Reality?
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Objective Collapse Theories offer a explanation of quantum mechanics that is at once brand new and based in classical mechanics. In the world of quantum mechanics, it’s no big deal for particles to be in multiple different states at the same time, or to teleport between locations, or to influence each other faster than light. But somehow, none of this strangeness makes its way to the familiar scale of human beings — even though our world is made entirely of quantum-weird building blocks. The explanations of this transition range from the mystical influence of the conscious mind to the grandiose proposition of multiple realities. But Objective Collapse Theories feels as down to earth as the classical world that we’re trying to explain. Let’s see if it makes any sense.
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Graphene and an intense laser open the door to the extreme
Laser-driven ion acceleration has been studied to develop a compact and efficient plasma-based accelerator, which is applicable to cancer therapy, nuclear fusion, and high energy physics. Osaka University researchers, in collaboration with researchers at National Institutes for Quantum Science and Technology (QST), Kobe University, and National Central University in Taiwan, have reported direct energetic ion acceleration by irradiating the world’s thinnest and strongest graphene target with the ultra-intense J-KAREN laser at Kansai Photon Science Institute, QST in Japan. Their findings are published in Nature’s Scientific Reports.
It is known that a thinner target is required for higher ion energy in laser ion acceleration theory. However, it has been difficult to directly accelerate ions with an extremely thin target regime since the noise components of an intense laser destroy the targets before the main peak of the laser pulse. It is necessary to use plasma mirrors, which remove the noise components, to realize efficient ion acceleration with an intense laser.
Thus, the researchers have developed large-area suspended graphene (LSG) as a target of laser ion acceleration. Graphene is known as the world’s thinnest and strongest 2D material, which is suitable for laser-driven ion sources.