Lifeboat Foundation

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00:00 — Introduction.
00:38 — Landauer Limit.
02:51 — Quantum Computing.
04:21 — Human Brain Power?
07:03 — Turing Complete Universal Computation?
10:07 — Diminishing Returns.
12:08 — Byzantine Generals Problem.
14:38 — Terminal Race Condition.
17:28 — Metastasis.
20:20 — Polymorphism.
21:45 — Optimal Intelligence.
23:45 — Darwinian Selection “Survival of the Fastest“
26:55 — Speed Chess Metaphor.
29:42 — Conclusion & Recap.

Continue reading “My predictions about Artificial Super Intelligence (ASI)” »

This NISQ era of quantum computing is also the age where multiple approaches to quantum emerge. It’s akin to the moment before we decided to follow mostly through the x86 path. New research on fluxoni.

A research group led by Professor Kenji Ohmori at the Institute for Molecular Science, National Institutes of Natural Sciences are using an artificial crystal of 30,000 atoms aligned in a cubic array with a spacing of 0.5 micron, cooled to near absolute zero temperature. By manipulating the atoms with a special laser light that blinks for 10 picoseconds, they succeeded in executing quantum simulation of a model of magnetic materials.

Their novel “ultrafast quantum computer” scheme demonstrated last year was applied to quantum simulation. Their achievement shows that their novel “ultrafast quantum simulator” is an epoch-making platform, as it can avoid the issue of external noise, one of the biggest concerns for quantum simulators. The “ultrafast quantum simulator” is expected to contribute to the design of functional materials and the resolution of social problems.

Their results were published online in Physical Review Letters.

We often believe computers are more efficient than humans. After all, computers can complete a complex math equation in a moment and can also recall the name of that one actor we keep forgetting. However, human brains can process complicated layers of information quickly, accurately, and with almost no energy input: recognizing a face after only seeing it once or instantly knowing the difference between a mountain and the ocean. These simple human tasks require enormous processing and energy input from computers, and even then, with varying degrees of accuracy.

Creating brain-like computers with minimal energy requirements would revolutionize nearly every aspect of modern life. Funded by the Department of Energy, Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) — a nationwide consortium led by the University of California San Diego — has been at the forefront of this research.

UC San Diego Assistant Professor of Physics Alex Frañó is co-director of Q-MEEN-C and thinks of the center’s work in phases. In the first phase, he worked closely with President Emeritus of University of California and Professor of Physics Robert Dynes, as well as Rutgers Professor of Engineering Shriram Ramanathan. Together, their teams were successful in finding ways to create or mimic the properties of a single brain element (such as a neuron or synapse) in a quantum material.

The combination of nuclear magnetic resonance with first-principles calculations uncovers the stacking patterns of layers of a quantum material—information that could enable a deeper understanding of the material’s behavior.

Researchers have turned an optical lattice into a ratchet that moves atoms from one site to the next.

The 2024 Breakthrough Prize in Fundamental Physics goes to John Cardy and Alexander Zamolodchikov for their work in applying field theory to diverse problems.

Many physicists hear the words “quantum field theory,” and their thoughts turn to electrons, quarks, and Higgs bosons. In fact, the mathematics of quantum fields has been used extensively in other domains outside of particle physics for the past 40 years. The 2024 Breakthrough Prize in Fundamental Physics has been awarded to two theorists who were instrumental in repurposing quantum field theory for condensed-matter, statistical physics, and gravitational studies.

“I really want to stress that quantum field theory is not the preserve of particle physics,” says John Cardy, a professor emeritus from the University of Oxford. He shares the Breakthrough Prize with Alexander Zamolodchikov from Stony Brook University, New York.

A complete quantum computing system could be as large as a two-car garage when one factors in all the paraphernalia required for smooth operation. But the entire processing unit, made of qubits, would barely cover the tip of your finger.

Today’s smartphones, laptops and supercomputers contain billions of tiny electronic processing elements called transistors that are either switched on or off, signifying a 1 or 0, the binary language computers use to express and calculate all information. Qubits are essentially quantum transistors. They can exist in two well-defined states—say, up and down—which represent the 1 and 0. But they can also occupy both of those states at the same time, which adds to their computing prowess. And two—or more—qubits can be entangled, a strange quantum phenomenon where particles’ states correlate even if the particles lie across the universe from each other. This ability completely changes how computations can be carried out, and it is part of what makes quantum computers so powerful, says Nathalie de Leon, a quantum physicist at Princeton University. Furthermore, simply observing a qubit can change its behavior, a feature that de Leon says might create even more of a quantum benefit. “Qubits are pretty strange. But we can exploit that strangeness to develop new kinds of algorithms that do things classical computers can’t do,” she says.

Scientists are trying a variety of materials to make qubits. They range from nanosized crystals to defects in diamond to particles that are their own antiparticles. Each comes with pros and cons. “It’s too early to call which one is the best,” says Marina Radulaski of the University of California, Davis. De Leon agrees. Let’s take a look.