Lifeboat Foundation

Some problems of the very intuitive evolutionary emergentist paradigm trying to explain consciousness from neurons, thanks to Andrés Gómez Emilsson and Chris Percy at Qualia Research Institute:

The “Slicing Problem” is a thought experiment that raises questions for substrate-neutral computational theories of consciousness, particularly, in functionalist approaches.

The thought experiment uses water-based logic gates to construct a computer in a way that permits cleanly slicing each gate and connection in half, creating two identical computers each instantiating the same computation. The slicing can be reversed and repeated via an on/off switch, without changing the amount of matter in the system.

Popular Summary.

Most currently used quantum neural network architectures have little-to-no inductive biases, leading to trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in classical machine learning address this crux by creating models encoding the symmetries of the learning task. This is materialized through the usage of equivariant neural networks whose action commutes with that of the symmetry.

In this work, we import these ideas to the quantum realm by presenting a general theoretical framework to understand, classify, design, and implement equivariant quantum neural networks. As a special implementation, we show how standard quantum convolutional neural networks (QCNN) can be generalized to group-equivariant QCNNs where both the convolutional and pooling layers are equivariant under the relevant symmetry group.

Ever since superconductivity was discovered in the early 1900s, it has both captivated and mystified scientists. Superconductors conduct electricity with virtually zero resistance, allowing for highly efficient transmission of electrical currents. Among other uses, they create the strong magnetic fields we depend on for medical imaging with MRI machines.

The first known superconductor, mercury, only works when the temperature dips just below-450 F. Copper-containing materials called cuprates were found in the ’80s to become superconductors at warmer temperatures, though still inconveniently cold — closer to-200 F. Understanding how these so-called high-temperature superconductors work could eventually lead to ones that can operate in less frigid conditions.

One potential hallmark of high-temperature superconductors has remained purely theoretical, until now. A team of scientists, including several from the U.S. Department of Energy’s (DOE) Argonne National Laboratory, has observed an elusive state of matter called quantum spin nematic. The study, which was published in the journal Nature (“Quantum spin nematic phase in a square-lattice iridate”), used the Advanced Photon Source (APS), a DOE Office of Science user facility at Argonne that also happens to use superconductors. The results lend insight on both high-temperature superconductivity and some of the physics involved in quantum computing.

Dementia has become rampant among human beings who are pushed into a deep mental abyss, devoid of memories and remembrance.

It has been termed as “the long goodbye”. Even though the person remains alive, memories fade away slowly and irreversibly due to dementia.

Dementia eventually snatches away the ability of a person to communicate, eat and drink on their own, recognise family members and understand where they are.

A particle-beam-generating method—called wakefield acceleration—uses proton bunches, which can fragment into high-density filaments as a result of their interactions with plasma, new experiments show.

Superradiant atoms offer a groundbreaking method for measuring time with an unprecedented level of precision. In a recent study published by the scientific journal Nature Communications, researchers from the University of Copenhagen present a new method for measuring the time interval, seconds, that overcomes some of the limitations that even today’s most advanced atomic clocks encounter. This advancement could have broad implications in areas such as space exploration, volcanic monitoring, and GPS systems.

The second, which is the most precisely defined unit of measurement, is currently measured by atomic clocks in different places around the world that together tell us what time it is. Using radio waves, atomic clocks continuously send signals that synchronize our computers, phones, and watches.

Oscillations are the key to keeping time. In a grandfather clock, these oscillations are from a pendulum’s swinging from side to side every second, while in an atomic clock, it is a laser beam that corresponds to an energy transition in strontium and oscillates about a million billion times per second.

Researchers at AMOLF, working alongside colleagues from Germany, Switzerland, and Austria, have realized a new type of metamaterial through which sound waves flow in an unprecedented fashion. It provides a novel form of amplification of mechanical vibrations, which has the potential to improve sensor technology and information processing devices.

This metamaterial is the first instance of a so-called ‘bosonic Kitaev chain’, which gets its special properties from its nature as a topological material. It was realized by making nanomechanical resonators interact with laser light through radiation pressure forces. The discovery, which is published on March 27 in the renowned scientific journal Nature, was achieved in an international collaboration between AMOLF, the Max Planck Institute for the Science of Light, the University of Basel, ETH Zurich, and the University of Vienna.

The ‘Kitaev chain’ is a theoretical model that describes the physics of electrons in a superconducting material, specifically a nanowire. The model is famous for predicting the existence of special excitations at the ends of such a nanowire: Majorana zero modes. These have gained intense interest because of their possible use in quantum computers.

A new electrical power converter design developed by Kobe University offers significantly improved efficiency at a reduced cost and lower maintenance. This direct current voltage boost converter is set to make a substantial impact on the development of electric and electronic components in various sectors, including power generation, healthcare, mobility, and information technology.

Devices that harvest energy from sunlight or vibrations, or power medical devices or hydrogen-fueled cars have one key component in common. This so-called “boost converter” converts low-voltage direct current input into high-voltage direct current output. Because it is such a ubiquitous and key component, it is desirable that it uses as few parts as possible for reduced maintenance and cost and at the same time that it operates at the highest possible efficiency without generating electromagnetic noise or heat. The main working principle of boost converters is to quickly change between two states in a circuit, one that stores energy and another that releases it. The faster the switching is, the smaller the components can be and therefore the whole device can be downsized. However, this also increases the electromagnetic noise and heat production, which deteriorates the performance of the power converter.

The team of Kobe University power electronics researcher Mishima Tomokazu made significant progress in developing a new direct current power conversion circuit. They managed to combine high-frequency switching (about 10 times higher than before) with a technique that reduces electromagnetic noise and power losses due to heat dissipation, called “soft switching,” while also reducing the number of components and, therefore, keeping cost and complexity low.

Argonne National Laboratory scientists have used anomaly detection in the ATLAS collaboration to search for new particles, identifying a promising anomaly that could indicate new physics beyond the Standard Model.

Scientists used a neural network, a type of brain-inspired machine learning algorithm, to sift through large volumes of particle collision data in a study that marks the first use of a neural network to analyze data from a collider experiment.

Particle physicists are tasked with mining this massive and growing store of collision data for evidence of undiscovered particles. In particular, they’re searching for particles not included in the Standard Model of particle physics, our current understanding of the universe’s makeup that scientists suspect is incomplete.