The speed of computation in a neutral-atom quantum computer is increased through the use of steerable excitation beams and fast, nondestructive state readout.
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New laser technique boosts power by individually controlling light modes
From precision machining to advanced microscopy, the demand for higher-power, ultrafast lasers continues to grow. Traditionally, researchers have relied on single-mode fibers to build these lasers, but they face a fundamental physical limit on energy output. To break through this bottleneck, we have turned to multimode fibers, which can carry many light modes—essentially different shapes of light—at once, a technique known as spatiotemporal mode-locking (STML).
However, getting these different modes to work together in harmony has been a significant challenge. In our latest research, published in Optics Letters, we have developed a new technique that allows us to precisely and independently control each of these transverse modes, leading to a dramatic boost in laser power and versatility.
The core problem we faced is known as intermodal dispersion. In a multimode fiber, different light modes travel at slightly different speeds. This velocity mismatch causes the laser pulses to spread out and separate in time and space, preventing the formation of stable, high-power pulses. Previous STML techniques typically used a method called spatial filtering to compensate for this dispersion, but this approach limits the number of modes that can be locked together, thereby capping the potential power enhancement.
A promising approach for the direct on-chip synthesis of boron nitride memristors
Two-dimensional (2D) materials, thin crystalline substances only a few atoms thick, have numerous advantageous properties compared to their three-dimensional (3D) bulk counterparts. Most notably, many of these materials allow electricity to flow through them more easily than bulk materials, have tunable bandgaps, are often also more flexible and better suited for fabricating small, compact devices.
Past studies have highlighted the promise of 2D materials for creating advanced systems, including devices that perform computations emulating the functioning of the brain (i.e., neuromorphic computing systems) and chips that can both process and store information (i.e., in-memory computing systems). One material that has been found to be particularly promising is hexagonal boron nitride (hBN), which is made up of boron and nitrogen atoms arranged in a honeycomb lattice resembling that of graphene.
This material is an excellent insulator, has a wide bandgap that makes it transparent to visible light, a good mechanical strength, and retains its performance at high temperatures. Past studies have demonstrated the potential of hBN for fabricating memristors, electronic components that can both store and process information, acting both as memories and as resistors (i.e., components that control the flow of electrical current in electronic devices).
RoboBall: Ball-like robot could easily map steep moon craters
The RoboBall project is based on the simple concept of a “robot in an airbag,” with two versions currently in development.
Elon Musk’s Drops Hints About His Next Master Plan
Questions to inspire discussion.
AI and Supercomputing Developments.
🖥️ Q: What is XAI’s Colossus 2 and its significance? A: XAI’s Colossus 2 is planned to be the world’s first gigawatt-plus AI training supercomputer, with a non-trivial chance of achieving AGI (Artificial General Intelligence).
⚡ Q: How does Tesla plan to support the power needs of Colossus 2? A: Elon Musk plans to build power plants and battery storage in America to support the massive power requirements of the AI training supercomputer.
💰 Q: What is Musk’s prediction for universal income by 2030? A: Musk believes universal high income will be achieved, providing everyone with the best medical care, food, home, transport, and other necessities.
🏭 Q: How does Musk plan to simulate entire companies with AI? A: Musk aims to simulate entire companies like Microsoft with AI, representing a major jump in AI capabilities but limited to software replication, not complex physical products.
Freeze-framing the cellular world to capture a fleeting moment of activity
Now, in an article published in Light: Science & Applications, researchers from The University of Osaka, together with collaborating institutions, have unveiled a cryo–optical microscopy technique that takes a high-resolution, quantitatively accurate snapshot at a precisely selected timepoint in dynamic cellular activity.
Capturing fast dynamic cellular events with spatial detail and quantifiability has been a major challenge, owing to a fundamental trade-off between temporal resolution and the “photon budget,” that is, how much light can be collected for the image. With limited photons and only dim, noisy images, important features in both space and time become lost in the noise.
“Instead of chasing speed in imaging, we decided to freeze the entire scene,” explains one of the lead authors, Kosuke Tsuji. “We developed a special sample-freezing chamber to combine the advantages of live-cell and cryo-fixation microscopy. By rapidly freezing live cells under the optical microscope, we could observe a frozen snapshot of the cellular dynamics at high resolutions.”
AI-enhanced technique assembles defect-free arrays with thousands of atoms
The simulation of quantum systems and the development of systems that can perform computations leveraging quantum mechanical effects rely on the ability to arrange atoms in specific patterns with high levels of precision. To arrange atoms in ordered patterns known as arrays, physicists typically use optical tweezers, highly focused laser beams that can trap particles.