Lifeboat Foundation

The nanoscale electronic parts in devices like smartphones are solid, static objects that once designed and built cannot transform into anything else. But University of California, Irvine physicists have reported the discovery of nanoscale devices that can transform into many different shapes and sizes even though they exist in solid states.

It’s a finding that could fundamentally change the nature of electronic devices, as well as the way scientists research atomic-scale quantum materials. The study is published in Science Advances.

“What we discovered is that for a particular set of materials, you can make nanoscale electronic devices that aren’t stuck together,” said Javier Sanchez-Yamagishi, an assistant professor of physics & astronomy whose lab performed the new research. “The parts can move, and so that allows us to modify the size and shape of a device after it’s been made.”

The Singularity is a technological event horizon beyond which we cannot see – a moment in future history when exponential progress makes the impossible possible. This video discusses the concept of the Singularity, related technologies including AI, synthetic biology, cybernetics and quantum computing, and their potential implications.

My previous video “AI, Robots & the Future” is here:
https://www.youtube.com/watch?v=iaGIo_Viazs.

Continue reading “Explaining the Singularity” »

Human decision-making is relevant for concept formation and cognitive illusions. Cognitive illusions can be explained by quantum probability, while the reason for introducing quantum mechanics is based on ad hoc bounded rationality (BR). Concept formation can be explained in a set-theoretic way, although such explanations have not been extended to cognitive illusions. We naturally expand the idea of BR to incomplete BR and introduce the key notion of nonlocality in cognition without any attempts on quantum theory. We define incomplete bounded rationality and nonlocality as a binary relation, construct a lattice from the relation by using a rough-set technique, and define probability in concept formation. By using probability defined in concept formation, we describe various cognitive illusions, such as the guppy effect, conjunction fallacy, order effect, and so on.

😗 year 2010 :3.

(PhysOrg.com) — Scientists have literally taken a leap into a new era of computing power by making the world’s smallest precision-built transistor — a “quantum dot” of just seven atoms in a single silicon crystal. Despite its incredibly tiny size — a mere four billionths of a metre long — the quantum dot is a functioning electronic device, the world’s first created deliberately by placing individual atoms.

It can be used to regulate and control electrical current flow like a commercial transistor but it represents a key step into a new age of atomic-scale miniaturisation and super-fast, super-powerful computers.

Continue reading “Quantum leap: World’s smallest transistor built with just 7 atoms” »

Year 2022 😗

Argonne National Laboratory, Lemont, IL

A team of scientists at the U.S. Department of Energy’s Argonne National Laboratory, have achieved efficient quantum coupling between two distant magnetic devices, which can host a certain type of magnetic excitations called magnons. These excitations happen when an electric current generates a magnetic field. Coupling allows magnons to exchange energy and information. This kind of coupling may be useful for creating new quantum information technology devices.

Continue reading “Tiny Magnets to Create Miniaturizable Quantum Devices” »

face_with_colon_three year 2022.

For the first time, scientists have created a quantum computing experiment for studying the dynamics of wormholes – that is, shortcuts through spacetime that could get around relativity’s cosmic speed limits.

Wormholes are traditionally the stuff of science fiction, ranging from Jodie Foster’s wild ride in Contact to the time-bending plot twists in Interstellar. But the researchers behind the experiment, reported in the December 1 issue of the journal Nature, hope that their work will help physicists study the phenomenon for real.

Continue reading “A ‘Wormhole’ Built on a Quantum Computer Teleported Information as Predicted” »

The QCD vacuum (i.e., the ground state of vacuum in the quantum chromodynamics regime) is theoretically characterized by the presence of non-zero expectation values of condensates, such as gluons and quark–antiquark pairs. Instead of being associated with a lack of particles and interactions in an empty space, physics theory regards this state as filled with the so-called condensates, which have the same quantum numbers as the vacuum and cannot be directly observed.

While many theoretical physicists have discussed the properties of the QCD vacuum, experimentally validating these theoretical predictions has so far proved challenging, simply because the condensates in this state are elusive and cannot be directly detected. A hint of experimental “observation” can be found in the theoretical predictions of the properties of the QCD vacuum.

Theories predict that the condensate may decrease in the high temperature and/or at a high matter density due to the partial restoration of the so-called chiral symmetry. To prove these theories, some researchers collected measurements during ultra-relativistic, head-on collisions of heavy ions at particularly high temperatures. Other efforts in this area tried to probe properties of the QCD vacuum by measuring so-called “medium effects.” These are essentially effects that alter the QCD vacuum and its structure, prompted by the presence of high matter density such as nuclear matter.

Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.

Several researchers have proposed models that combine artificial neural networks (ANNs) or quantum neural networks (QNNs) with various other ingredients. For example, Haykin (1999) and Bishop (1995) developed multilevel activation function QNNs using the quantum linear superposition feature (Bonnell and Papini, 1997).

The prime factorization algorithm of Shor was used to illustrate the basic workings of QNNs (Shor, 1994). Shor’s algorithm uses quantum computations by quantum gates to provide the potential power for quantum computers (Bocharov et al., 2017; Dridi and Alghassi, 2017; Demirci et al., 2018; Jiang et al., 2018). Meanwhile, the work of Kak (1995) focused on the relationship between quantum mechanics principles and ANNs. Kak introduced the first quantum network based on the principles of neural networks, combining quantum computation with convolutional neural networks to produce quantum neural computation (Kak, 1995; Zhou, 2010). Since then, a myriad of QNN models have been proposed, such as those of Zhou (2010) and Schuld et al. (2014).

A system for realizing many-photon quantum circuits is presented, comprising a programmable nanophotonic chip operating at room temperature, interfaced with a fully automated control system.