Lifeboat Foundation

AI researchers connect machine learning to Godel’s Incompleteness Theorem via a finding of unLearnability.

Simple artificial-intelligence problem puts researchers up against a logical paradox discovered by famed mathematician Kurt Gödel.

“Open Article” smile Spin-based quantum computers have the potential to tackle difficult mathematical problems that cannot be solved using ordinary computers, but many problems remain in making these machines scalable. Now, an international group of researchers led by the RIKEN Center for Emergent Matter Science have crafted a new architecture for quantum computing. By constructing a hybrid device made from two different types of qubit—the fundamental computing element of quantum computers –they have created a device that can be quickly initialized and read out, and that simultaneously maintains high control fidelity.

Single-spin qubits in semiconductor quantum dots hold promise for universal quantum computation with demonstrations of a high single-qubit gate fidelity above 99.9% and two-qubit gates in conjunction with a long coherence time. However, initialization and readout of a qubit is orders of magnitude slower than control, which is detrimental for implementing measurement-based protocols such as error-correcting codes. In contrast, a singlet-triplet qubit, encoded in a two-spin subspace, has the virtue of fast readout with high fidelity. Here, we present a hybrid system which benefits from the different advantages of these two distinct spin-qubit implementations. A quantum interface between the two codes is realized by electrically tunable inter-qubit exchange coupling. We demonstrate a controlled-phase gate that acts within 5.5 ns, much faster than the measured dephasing time of 211 ns. The presented hybrid architecture will be useful to settle remaining key problems with building scalable spin-based quantum computers.

“Push your limits, get out of your comfort zone, and pick tasks that are harder than what you’re used to,” she says.

Science wasn’t always her first choice.

By Christa I. De La Cruz

Continue reading “Meet the Filipina Engineer Who Struggled in Math But Now Works at NASA” »

‘’As a result, it’s nonsensical to ask what happens to space-time beyond the Cauchy horizon because space-time, as it’s regarded within the theory of general relativity, no longer exists. “This gives one a way out of this philosophical conundrum,” said Dafermos.

Mathematicians have disproved the strong cosmic censorship conjecture. Their work answers one of the most important questions in the study of general relativity and changes the way we think about space-time.

Digital security depends on the difficulty of factoring large numbers. A new proof shows why one method for breaking digital encryption won’t work.

Scientists at the University of Würzburg have been able to boost current super-resolution microscopy by a novel tweak. They coated the glass cover slip as part of the sample carrier with tailor-made biocompatible nanosheets that create a mirror effect. This method shows that localizing single emitters in front of a metal-dielectric coating leads to higher precision, brightness and contrast in Single Molecule Localization Microscopy (SMLM). The study was published in the Nature journal Light: Science and Applications.

The sharpness of a light microscope is limited by physical conditions—structures that are closer together than 0.2 thousandths of a millimeter blur, and can no longer be distinguished from each other. The cause of this blurring is diffraction. Each point-shaped object is therefore not shown as a point, but as a blurry spot.

With mathematical methods, the resolution can still be drastically improved. One method would calculate its exact center from the brightness distribution of the blurry spot. However, it only works if two closely adjacent points of the object are initially not simultaneously but subsequently visible, and are merged later in the image processing. This temporal decoupling prevents superimposition of the blurry spot. For years, researchers in life sciences have been using this tricky method for super high-resolution light microscopy of cells.

by Eloisa Marchesoni

Today, I will talk about the recent creation of really intelligent machines, able to solve difficult problems, to recreate the creativity and versatility of the human mind, machines not only able to excel in a single activity but to abstract general information and find solutions that are unthinkable for us. I will not talk about blockchain, but about another revolution (less economic and more mathematical), which is all about computing: quantum computers.

Quantum computing is not really new, as we have been talking about it for a couple of decades already, but we are just now witnessing the transition from theory to realization of such technology. Quantum computers were first theorized at the beginning of the 1980s, but only in the last few years, thanks to the commitment of companies like Google and IBM, a strong impulse has been pushing the development of these machines. The quantum computer is able to use quantum particles (imagine them to be like electrons or photons) to process information. The particles act as positive or negative (i., the 0 and the 1 that we are used to see in traditional computer science) alternatively or at the same time, thus generating quantum information bits called “qubits”, which can have value either 0 or 1 or a quantum superposition of 0 and 1.

Continue reading “How developments in Quantum Computing could affect cryptocurrencies” »

How atoms arrange themselves at the smallest scale was thought to follow a ‘drum-skin’ rule, but mathematicians have now found a simpler solution.

Atomic arrangements in different materials can provide a lot of information about the properties of materials, and what the potential is for altering what they can be used for.

However, where two materials touch – at their interface – complex interactions arise that make predicting the arrangement of atoms difficult.

Deep learning has been making it possible for powerful machines to approximate and imitate abilities and techniques once thought to be uniquely human. Mathematicians have struggled to explain how they work so well and may now get some answers by looking outside mathematics and into the nature of the universe.