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Archive for the ‘information science’ category: Page 63

Apr 23, 2023

Quantum circuit learning as a potential algorithm to predict experimental chemical properties

Posted by in categories: chemistry, information science, quantum physics

We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo-and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth non-linear functions from small datasets (100 records). Compared with conventional algorithms, such as random forest, support vector machine, and linear regressions, the QCL can offer better predictions with some one-dimensional functions and experimental chemical databases. QCL will potentially help the virtual exploration of new molecules and materials more efficiently through its superior prediction performances.

Apr 22, 2023

The Multiverse: Our Universe Is Suspiciously Unlikely to Exist—Unless It Is One of Many

Posted by in categories: alien life, information science, particle physics

But we expect that it’s in that first tiny fraction of a second that the key features of our universe were imprinted.

The conditions of the universe can be described through its “fundamental constants”—fixed quantities in nature, such as the gravitational constant (called G) or the speed of light (called C). There are about 30 of these representing the sizes and strengths of parameters such as particle masses, forces, or the universe’s expansion. But our theories don’t explain what values these constants should have. Instead, we have to measure them and plug their values into our equations to accurately describe nature.

Continue reading “The Multiverse: Our Universe Is Suspiciously Unlikely to Exist—Unless It Is One of Many” »

Apr 21, 2023

Artificial intelligence has improved the first-ever real photo of a supermassive black hole 6.5 billion times heavier than the Sun

Posted by in categories: cosmology, information science, robotics/AI

In 2017, the European Southern Observatory (ESO) obtained the first ever real photo of a black hole. Six years later, artificial intelligence was able to improve the image.

Here’s What We Know

American scientists have decided to improve the photo of a black hole. The original image shows something resembling a “fuzzy donut”. Experts have applied the PRIMO algorithm, based on machine learning, to improve the image.

Apr 21, 2023

Giant orbital magnetic moment appears in a graphene quantum dot

Posted by in categories: computing, information science, particle physics, quantum physics

A giant orbital magnetic moment exists in graphene quantum dots, according to new work by physicists at the University of California Santa Cruz in the US. As well as being of fundamental interest for studying systems with relativistic electrons – that is those travelling at near-light speeds – the work could be important for quantum information science since these moments could encode information.

Graphene, a sheet of carbon just one atom thick, has a number of unique electronic properties, many of which arise from the fact that it is a semiconductor with a zero-energy gap between its valence and conduction bands. Near where the two bands meet, the relationship between the energy and momentum of charge carriers (electrons and holes) in the material is described by the Dirac equation and resembles that of a photon, which is massless.

These bands, called Dirac cones, enable the charge carriers to travel through graphene at extremely high, “ultra-relativistic” speeds approaching that of light. This extremely high mobility means that graphene-based electronic devices such as transistors could be faster than any that exist today.

Apr 20, 2023

Is deep learning a necessary ingredient for artificial intelligence?

Posted by in categories: information science, robotics/AI, transportation

The earliest artificial neural network, the Perceptron, was introduced approximately 65 years ago and consisted of just one layer. However, to address solutions for more complex classification tasks, more advanced neural network architectures consisting of numerous feedforward (consecutive) layers were later introduced. This is the essential component of the current implementation of deep learning algorithms. It improves the performance of analytical and physical tasks without human intervention, and lies behind everyday automation products such as the emerging technologies for self-driving cars and autonomous chat bots.

The key question driving new research published today in Scientific Reports is whether efficient learning of non-trivial classification tasks can be achieved using brain-inspired shallow feedforward networks, while potentially requiring less .

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Apr 20, 2023

What’s AGI, and Why Are AI Experts Skeptical?

Posted by in categories: information science, robotics/AI

ChatGPT and other bots have revived conversations on artificial general intelligence. Scientists say algorithms won’t surpass you any time soon.

Apr 19, 2023

Algorithms Simulate Infinite Quantum System on Finite Quantum Computers

Posted by in categories: computing, information science, quantum physics

Year 2021 😗😁


Researchers say algorithms can simulate an infinite quantum system on finite quantum computers in interesting advance for quantum tech.

Apr 19, 2023

A defence of human uniqueness against AI encroachment, with Kenn Cukier

Posted by in categories: economics, employment, information science, robotics/AI, singularity

Despite the impressive recent progress in AI capabilities, there are reasons why AI may be incapable of possessing a full “general intelligence”. And although AI will continue to transform the workplace, some important jobs will remain outside the reach of AI. In other words, the Economic Singularity may not happen, and AGI may be impossible.

These are views defended by our guest in this episode, Kenneth Cukier, the Deputy Executive Editor of The Economist newspaper.

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Apr 19, 2023

A neuromorphic visual sensor can recognize moving objects and predict their path

Posted by in categories: information science, robotics/AI

A new bio-inspired sensor can recognize moving objects in a single frame from a video and successfully predict where they will move to. This smart sensor, described in a Nature Communications paper, will be a valuable tool in a range of fields, including dynamic vision sensing, automatic inspection, industrial process control, robotic guidance, and autonomous driving technology.

Current motion detection systems need many components and complex algorithms doing frame-by-frame analyses, which makes them inefficient and energy-intensive. Inspired by the human visual system, researchers at Aalto University have developed a new neuromorphic vision technology that integrates sensing, memory, and processing in a single device that can detect motion and predict trajectories.

At the core of their technology is an array of photomemristors, that produce in response to light. The current doesn’t immediately stop when the light is switched off. Instead, it decays gradually, which means that photomemristors can effectively “remember” whether they’ve been exposed to light recently. As a result, a sensor made from an array of photomemristors doesn’t just record instantaneous information about a scene, like a camera does, but also includes a dynamic memory of the preceding instants.

Apr 18, 2023

Room-temperature superfluidity in a polariton condensate Physics

Posted by in categories: energy, information science, mapping, mathematics, quantum physics, space

face_with_colon_three year 2017.


First observed in liquid helium below the lambda point, superfluidity manifests itself in a number of fascinating ways. In the superfluid phase, helium can creep up along the walls of a container, boil without bubbles, or even flow without friction around obstacles. As early as 1938, Fritz London suggested a link between superfluidity and Bose–Einstein condensation (BEC)3. Indeed, superfluidity is now known to be related to the finite amount of energy needed to create collective excitations in the quantum liquid4,5,6,7, and the link proposed by London was further evidenced by the observation of superfluidity in ultracold atomic BECs1,8. A quantitative description is given by the Gross–Pitaevskii (GP) equation9,10 (see Methods) and the perturbation theory for elementary excitations developed by Bogoliubov11. First derived for atomic condensates, this theory has since been successfully applied to a variety of systems, and the mathematical framework of the GP equation naturally leads to important analogies between BEC and nonlinear optics12,13,14. Recently, it has been extended to include condensates out of thermal equilibrium, like those composed of interacting photons or bosonic quasiparticles such as microcavity exciton-polaritons and magnons14,15. In particular, for exciton-polaritons, the observation of many-body effects related to condensation and superfluidity such as the excitation of quantized vortices, the formation of metastable currents and the suppression of scattering from potential barriers2,16,17,18,19,20 have shown the rich phenomenology that exists within non-equilibrium condensates. Polaritons are confined to two dimensions and the reduced dimensionality introduces an additional element of interest for the topological ordering mechanism leading to condensation, as recently evidenced in ref. 21. However, until now, such phenomena have mainly been observed in microcavities embedding quantum wells of III–V or II–VI semiconductors. As a result, experiments must be performed at low temperatures (below ∼ 20 K), beyond which excitons autoionize. This is a consequence of the low binding energy typical of Wannier–Mott excitons. Frenkel excitons, which are characteristic of organic semiconductors, possess large binding energies that readily allow for strong light–matter coupling and the formation of polaritons at room temperature. Remarkably, in spite of weaker interactions as compared to inorganic polaritons22, condensation and the spontaneous formation of vortices have also been observed in organic microcavities23,24,25. However, the small polariton–polariton interaction constants, structural inhomogeneity and short lifetimes in these structures have until now prevented the observation of behaviour directly related to the quantum fluid dynamics (such as superfluidity). In this work, we show that superfluidity can indeed be achieved at room temperature and this is, in part, a result of the much larger polariton densities attainable in organic microcavities, which compensate for their weaker nonlinearities.

Our sample consists of an optical microcavity composed of two dielectric mirrors surrounding a thin film of 2,7-Bis[9,9-di(4-methylphenyl)-fluoren-2-yl]-9,9-di(4-methylphenyl)fluorene (TDAF) organic molecules. Light–matter interaction in this system is so strong that it leads to the formation of hybrid light–matter modes (polaritons), with a Rabi energy 2 ΩR ∼ 0.6 eV. A similar structure has been used previously to demonstrate polariton condensation under high-energy non-resonant excitation24. Upon resonant excitation, it allows for the injection and flow of polaritons with a well-defined density, polarization and group velocity.

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