Lifeboat Foundation

In 2022, a nuclear-fusion experiment yielded more energy than was delivered by the lasers that ignited the fusion reaction (see Viewpoint: Nuclear-Fusion Reaction Beats Breakeven). That demonstration was an example of indirect-drive inertial-confinement fusion, in which lasers collapse a fuel pellet by heating a gold can that surrounds it. This approach is less efficient than heating the pellet directly since the pellet absorbs less of the lasers’ energy. Nevertheless, it has been favored by researchers at the largest laser facilities because it is less sensitive to nonuniform laser illumination. Now Duncan Barlow at the University of Bordeaux, France, and his colleagues have devised an efficient way to improve illumination uniformity in direct-drive inertial-confinement fusion [1]. This advance helps overcome a remaining barrier to high-yield direct-drive fusion using existing facilities.

Triggering self-sustaining fusion by inertial confinement requires pressures and temperatures that are achievable only if the fuel pellet implodes with high uniformity. Such uniformity can be prevented by heterogeneities in the laser illumination and in the way the beams interact with the resulting plasma. Usually, researchers identify the laser configuration that minimizes these heterogeneities by iterating radiation-hydrodynamics simulations that are computationally expensive and labor intensive. Barlow and his colleagues developed an automatic, algorithmic approach that bypasses the need for such iterative simulations by approximating some of the beam–plasma interactions.

Compared with an experiment using a spherical, plastic target at the National Ignition Facility in California, the team’s optimization method should deliver an implosion that reaches 2 times the density and 3 times the pressure. But the approach can also be applied to other pellet geometries and at other facilities.

In a world powered by artificial intelligence applications, data is king, but it’s also the crown’s biggest burden.

As described in the article, quantum memory stores data in ways that classical memory systems cannot match. In quantum systems, information is stored in quantum states, using the principles of superposition and entanglement to represent data more efficiently. This ability allows quantum systems to process and store vastly more information, potentially impacting data-heavy industries like AI.

In a 2021 study from the California Institute of Technology, researchers showed that quantum memory could dramatically reduce the number of steps needed to model complex systems. Their method proved that quantum algorithms using memory could require exponentially fewer steps, cutting down on both time and energy. However, this early work required vast amounts of quantum memory—an obstacle that could have limited its practical application.

Continue reading “The Data Dilemma: How Quantum Memory Could Ease the Energy Demands of Computing” »

Predicting the behavior of many interacting quantum particles is a complex task, but it’s essential for unlocking the potential of quantum computing in real-world applications. A team of researchers, led by EPFL, has developed a new method to compare quantum algorithms and identify the most challenging quantum problems to solve.

Quantum systems, from subatomic particles to complex molecules, hold the key to understanding the workings of the universe. However, modeling these systems quickly becomes overwhelming due to their immense complexity. It’s like trying to predict the behavior of a massive crowd where everyone constantly influences everyone else. When you replace the crowd with quantum particles, you encounter what’s known as the “quantum many-body problem.”

Quantum many-body problems involve predicting the behavior of numerous interacting quantum particles. Solving these problems could lead to major breakthroughs in fields like chemistry and materials science, and even accelerate the development of technologies like quantum computers.

In 2005, the futurist Ray Kurzweil predicted that by 2045, machines would become smarter than humans. He called this inflection point the “singularity,” and it struck a chord. Kurzweil, who’s been tracking artificial intelligence since 1963, gained a fanatical following, especially in Silicon Valley.

Now comes The Singularity is Nearer: When We Merge with A.I. where Kurzweil steps up the Singularity’s arrival timeline to 2029. “Algorithmic innovations and the emergence of big data have allowed AI to achieve startling breakthroughs sooner than expected,” reports Kurzweil. From winning at games like Jeopardy! and Go to driving automobiles, writing essays, passing bar exams, and diagnosing cancer, chunks of the Singularity are arriving daily, and there’s more good news just ahead.

Very soon, predicts Kurzweil, artificial general intelligence will be able to do anything a human can do, only better. Expect 3D printed clothing and houses by the end of this decade. Look for medical cures that will “add decades to human life spans” just ahead. “These are the most exciting and momentous years in all of history,” Kurzweil noted in an interview with Boston Globe science writer Brian Bergstein.

A team of Chinese researchers, led by Wang Chao from Shanghai University, has demonstrated that D-Wave’s quantum annealing computers can crack encryption methods that safeguard sensitive global data.

This breakthrough, published in the Chinese Journal of Computers, emphasizes that quantum machines are closer than expected to threatening widely used cryptographic systems, including RSA and Advanced Encryption Standard (AES).

The research team’s experiments focused on leveraging D-Wave’s quantum technology to solve cryptographic problems. In their paper, titled “Quantum Annealing Public Key Cryptographic Attack Algorithm Based on D-Wave Advantage,” the researchers explained how quantum annealing could transform cryptographic attacks into combinatorial optimization problems, making them more manageable for quantum systems.

How do we assess quantum advantage when exact classical solutions are not available?

A quantum advantage is a demonstration of a solution for a problem for which a quantum computer can provide a demonstrable improvement over any classical method and classical resources in terms of accuracy, runtime…

Today, algorithms designed to solve this problem mostly rely on what we call variational methods, which are algorithms guaranteed to output an energy for a target system which cannot be lower than the exact solution — or the deepest valley — up to statistical uncertainties. An ideal quality metric for the ground state problem would not only allow the user to benchmark different methods against the same problem, but also different target problems when tackled by the same method.

Continue reading “Assessing quantum advantage for ground state problems” »

AI-powered misinformation detectors—artificial intelligence tools that identify false or inaccurate online content—have emerged as a potential intervention for helping internet users understand the veracity of the content they view. However, the algorithms used to create these detectors are experimental and largely untested at the scale necessary to be effective on a social media platform.

If it were up to Larry Ellison, the exorbitantly rich cofounder of software outfit Oracle, all of us will soon be smiling for the camera — constantly. Not for a cheery photograph, but to appease our super-invasive, if not totally omnipresent, algorithmic overseers.

As Business Insider reports, the tech centibillionaire glibly predicts that the wonders of AI will bring about a new paradigm of supercharged surveillance, guaranteeing that the proles — excuse us, “citizens” — all behave and stay in line.

“We’re going to have supervision,” Ellison said this week at an Oracle financial analysts meeting, per BI. “Every police officer is going to be supervised at all times, and if there’s a problem, AI will report that problem and report it to the appropriate person.”

The second ATLAS study, presented recently at the 17th International Workshop on Top Quark Physics, broke new ground by providing the first dedicated ATLAS measurement of how often top-quark pairs are produced along with jets originating from charm quarks (c-jets).

ATLAS physicists analyzed events with one or two leptons (electrons and muons), using a custom flavor-tagging algorithm developed specifically for this study to distinguish c-jets from b-jets and other jets. This algorithm was essential because c-jets are even more challenging to identify than b-jets, as they have shorter lifetimes and produce less distinct signatures in the ATLAS detector.

The study found that most theoretical models provided reasonable agreement with the data, though they generally underpredicted the production rates of c-jets. These results, which for the first time separately determined the cross-sections for single and multiple charm-quark production in top-quark-pair events, highlight the need for refined simulations of these processes to improve future measurements.