Lifeboat Foundation

A research team has constructed a coherent superposition of quantum evolution with two opposite directions in a photonic system and confirmed its advantage in characterizing input-output indefiniteness. The study was published in Physical Review Letters.

The notion that time flows inexorably from the past to the future is deeply rooted in people’s mind. However, the laws of physics that govern the motion of objects in the microscopic world do not deliberately distinguish the direction of time.

To be more specific, the basic equations of motion of both classical and quantum mechanics are reversible, and changing the direction of the time coordinate system of a dynamical process (possibly along with the direction of some other parameters) still constitutes a valid evolution process.

Researchers have developed a genetic algorithm for designing phononic crystal nanostructures, significantly advancing quantum computing and communications.

The new method, validated through experiments, allows precise control of acoustic wave propagation, promising improvements in devices like smartphones and quantum computers.

Quantum Computing Revolution

Robotic Autonomy in Complex Environments with Resiliency (RACER) program successfully tested autonomous movement on a new, much larger fleet vehicle – a significant step in scaling up the adaptability and capability of the underlying RACER algorithms.

The RACER Heavy Platform (RHP) vehicles are 12-ton, 20-foot-long, skid-steer tracked vehicles – similar in size to forthcoming robotic and optionally manned combat/fighting vehicles. The RHPs complement the 2-ton, 11-foot-long, Ackermann-steered, wheeled RACER Fleet Vehicles (RFVs) already in use.

Continue reading “RACER Speeds Into a Second Phase With Robotic Fleet Expansion and Another Experiment Success” »

A research team from Japan, including scientists from Hitachi, Ltd. (TSE 6,501, Hitachi), Kyushu University, RIKEN, and HREM Research Inc. (HREM), has achieved a major breakthrough in the observation of magnetic fields at unimaginably small scales.

In collaboration with National Institute of Advanced Industrial Science and Technology (AIST) and the National Institute for Materials Science (NIMS), the team used Hitachi’s atomic-resolution holography electron microscope—with a newly developed image acquisition technology and defocus correction algorithms—to visualize the magnetic fields of individual atomic layers within a crystalline solid.

Many advances in electronic devices, catalysis, transportation, and energy generation have been made possible by the development and adoption of high-performance materials with tailored characteristics. Atom arrangement and electron behavior are among the most critical factors that dictate a crystalline material’s properties.

Conventional encryption methods rely on complex mathematical algorithms and the limits of current computing power. However, with the rise of quantum computers, these methods are becoming increasingly vulnerable, necessitating quantum key distribution (QKD).

QKD is a technology that leverages the unique properties of quantum physics to secure data transmission. This method has been continuously optimized over the years, but establishing large networks has been challenging due to the limitations of existing quantum light sources.

In a new article published in Light: Science & Applications, a team of scientists in Germany have achieved the first intercity QKD experiment with a deterministic single-photon source, revolutionizing how we protect our confidential information from cyber threats.

The advent of quantum computers promises to revolutionize computing by solving complex problems exponentially more rapidly than classical computers. However, today’s quantum computers face challenges such as maintaining stability and transporting quantum information.

Phonons, which are quantized vibrations in periodic lattices, offer new ways to improve these systems by enhancing qubit interactions and providing more reliable information conversion. Phonons also facilitate better communication within quantum computers, allowing the interconnection of them in a network.

Nanophononic materials, which are artificial nanostructures with specific phononic properties, will be essential for next-generation quantum networking and communication devices. However, designing phononic crystals with desired vibration characteristics at the nano-and micro-scales remains challenging.

What is the future of generative AI compute?

— The future of generative AI compute involves embedding AI algorithms into the physics of the world to push the limits of density, spatial efficiency, and speed for AI, creating a full stack of software and hardware specifically designed for AI from first principles.

The first #Quantum #Supercomputers are here! Quantum enabled supercomputing promises to shed light on new quantum algorithms, hardware innovations, and error mitigation schemes. Large collaborations in the field are kicking off between corporations and supercomputing centers. Companies like NVIDIA, IBM, IQM, QuEra, and others are some of the earliest to participate in these partnerships.

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But deep learning has a massive drawback: The algorithms can’t justify their answers. Often called the “black box” problem, this opacity stymies their use in high-risk situations, such as in medicine. Patients want an explanation when diagnosed with a life-changing disease. For now, deep learning-based algorithms—even if they have high diagnostic accuracy—can’t provide that information.

To open the black box, a team from the University of Texas Southwestern Medical Center tapped the human mind for inspiration. In a study in Nature Computational Science, they combined principles from the study of brain networks with a more traditional AI approach that relies on explainable building blocks.

The resulting AI acts a bit like a child. It condenses different types of information into “hubs.” Each hub is then transcribed into coding guidelines for humans to read—CliffsNotes for programmers that explain the algorithm’s conclusions about patterns it found in the data in plain English. It can also generate fully executable programming code to try out.