While quantum computers are already being used for research in chemistry, material science, and data security, most are still too small to be useful for large-scale applications. A study led by researchers at the University of California, Riverside, now shows how “scalable” quantum architectures—systems made up of many small chips working together as one powerful unit—can be made.
Meta has developed a new flat ultra-thin panel laser display that could lead to lighter, more immersive augmented reality (AR) glasses and improve the picture quality of smartphones, tablets and televisions. The new display is only two millimeters thick and produces bright, high-resolution images.
Flat-panel displays, particularly those illuminated by LEDs, are ubiquitous, seen in everything from smartphones and televisions to laptops and computer monitors. But no matter how good the current technology is, the search for better is always ongoing. Lasers promise superior brightness and the possibility of making the technology smaller and more energy efficient by replacing bulky and power-hungry components with compact laser-based ones.
However, current laser displays still need large, complex optical systems to shine light onto screens. Previous attempts at making flat-panel laser displays have come up short as they required complex setups or were too difficult to manufacture in large quantities.
A research team has developed a device principle that can utilize “spin loss,” which was previously thought of as a simple loss, as a new power source for magnetic control.
The work is published in the journal Nature Communications.
Spintronics is a technology that utilizes the “spin” property of electrons to store and control information, and it is being recognized as a key foundation for next-generation information processing technologies such as ultra-low-power memory, neuromorphic chips, and computational devices for stochastic computation, as it consumes less power and is more nonvolatile than conventional semiconductors.
Researchers at VCU Massey Comprehensive Cancer Center have developed a new computational tool called Vesalius, which could help clinicians understand the complex relationships between cancer cells and their surrounding cells, leading to potential discoveries regarding the development of hard-to-treat cancers.
Findings from a new study, published in Nature Communications, could help guide the identification of predictive biomarkers for multiple cancers and better inform the effectiveness of different treatment options based on individuals’ specific type of disease.
Rajan Gogna, Ph.D., member of the Developmental Therapeutics research program at Massey and assistant professor in the VCU School of Medicine’s Department of Human and Molecular Genetics, and a team of collaborators were driven by the goal of interpreting extensive amounts of data in a meaningful way.
Quantum dots – semiconductor nanostructures that can emit single photons on demand – are considered among the most promising sources for photonic quantum computing.
However, every quantum dot is slightly different and may emit a slightly different color, according to a team at the University of Innsbruck, Austria, which has developed a technique to improve multi-photon state generation. The Innsbruck team states that, “the different forms of quantum dot means that, to produce multi-photon states we cannot use multiple quantum dots.”
Usually, researchers use a single quantum dot and multiplex the emission into different spatial and temporal modes, using a fast electro-optic modulator. But a contemporary technological challenge: faster electro-optic modulators are expensive and often require very customized engineering. To add to that, it may not be very efficient, which introduces unwanted losses in the system.
Nature Publishing: https://www.nature.com/articles/s41534-025-01083-0
Security wise: The team’s work combines years of research in quantum optics, semiconductor physics, and photonic engineering to open the door for next-generation quantum computers andunwanted losses in the system.
Communications. Here’s what you need to know. Securities IO: https://www.securities.io/passive-two-photon-quantum-dots-secure-communication
Researchers have developed QuantumShield-BC, a blockchain framework designed to resist attacks from quantum computers by integrating post-quantum cryptography (PQC) utilising algorithms such as Dilithium and SPHINCS+, quantum key distribution (QKD), and quantum Byzantine fault tolerance (Q-BFT) leveraging quantum random number generation (QRNG) for unbiased leader selection. The framework was tested on a controlled testbed with up to 100 nodes, demonstrating resistance to simulated quantum attacks and achieving fairness through QRNG-based consensus. An ablation study confirmed the contribution of each quantum component to overall security, although the QKD implementation was simulated and scalability to larger networks requires further investigation.
Slow data loads, memory-intensive joins, and long-running operations—these are problems every Python practitioner has faced. They waste valuable time and make iterating on your ideas harder than it should be.
This post walks through five common pandas bottlenecks, how to recognize them, and some workarounds you can try on CPU with a few tweaks to your code—plus a GPU-powered drop-in accelerator, cudf.pandas, that delivers order-of-magnitude speedups with no code changes.
Don’t have a GPU on your machine? No problem—you can use cudf.pandas for free in Google Colab, where GPUs are available and the library comes pre-installed.