Lifeboat Foundation

Artificial intelligence has been infiltrating our daily workflows and routine tasks for while now. It may be AI working in the background, as with Gemini’s integration across Google products, or you may be engaging more directly with popular content generators such as OpenAI’s ChatGPT and Dall-E. Looming in the not-too-distant future are amped-up virtual assistants.

As if AI itself weren’t futuristic enough, now there’s a whole new leap forward on the horizon: quantum AI. It’s a fusion of artificial intelligence with unconventional and still largely experimental quantum computing into a super-fast and highly efficient technology. Quantum computers will be the muscles, while AI will be the brains.

Here’s a quick breakdown of the basics to help you better understand quantum AI.

The SPINNING project, under the leadership of the Fraunhofer Institute, is pioneering a quantum computer using diamond-based spin photons, promising lower cooling requirements, longer operating times, and lower error rates compared to conventional quantum systems.

This innovative approach leverages the unique properties of diamonds to create stable qubits, aiming for high scalability and fidelity in quantum computing. Recent achievements include the successful demonstration of qubit entanglement over long distances, significantly outperforming traditional quantum computers in error rate and coherence time.

The SPINNING project: innovating with diamond-based technology.

A small twist allowed scientists to capture a rare quantum phase that has been under the shadows for decades.

“Wigner molecular crystals are important because they may exhibit novel transport and spin properties that could be useful for future quantum technologies such as quantum simulations,” researchers at the Lawrence Berkeley National Laboratory (LBL) note.

For the first time, LBL researchers have captured direct images of the Wigner molecular crystal using scanning tunneling microscopy (STM) —- an imaging technique that produces high-resolution visuals of materials at the atomic scale.

Continue reading “Scientists capture images of the cold ‘electron ice’ for first time” »

This was Mastercard in March: You probably do it every day without a second thought — shop online with your credit card, or install an update on your phone, or send a confidential file to a co-worker.

Mastercard’s efforts include a pilot to test whether quantum key distribution would work on its complex global network.

Quantum theory is distinguished by its apparent indeterminism, a feature that raises the question: Is this uncertainty inherent to Nature, or might…

Johannes Fankhauser

Continue reading “Epistemic Boundaries and Quantum Uncertainty: What Local Observers Can (Not) Predict” »

The combined results also speak to a more fundamental goal. For decades, the quantum computing community has been trying to establish quantum advantage —a task that quantum computers can do that a classical one would struggle with. Usually, researchers understand quantum advantage to mean that a quantum computer can do the task in far fewer steps.

The new papers show that quantum memory lets a quantum computer perform a task not necessarily with fewer steps, but with less data. As a result, researchers believe this in itself could be a way to prove quantum advantage. “It allows us to, in the more near term, already achieve that kind of quantum advantage,” said Hsin-Yuan Huang, a physicist at Google Quantum AI.

But researchers are excited about the practical benefits too, as the new results make it easier for researchers to understand complex quantum systems.

Did the laws of physics come into being at the Big Bang?

Watch the full talk at https://iai.tv/video/the-laws-of-physics-are-not-fixed-joao-…escription.

Continue reading “The laws of physics are not fixed | João Magueijo” »

Earlier this year, experiments shattered expectations by pushing the limits of what classical computing was believed to be capable of. Not only did the old fashioned binary technology crack a problem considered to be unique to quantum processing, it outperformed it.

Now physicists from the Flatiron Institute’s Center for Computational Quantum Physics in the US have an explanation for the feat which could help better define the boundaries between the two radically different methods of number-crunching.

The problem involves simulating the dynamics of what’s known as a transverse field Ising (TFI) model, which describes the alignment of quantum spin states between particles spread across a space.

Rapid advances in applying artificial intelligence to simulations in physics and chemistry have some people questioning whether we will even need quantum computers at all.