entangled dot cloud
MIT engineers develop a magnetic transistor for more energy-efficient electronics
Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly
Using microwave pulses to plug leaks in quantum computers makes them more reliable
Scientists have developed a new approach to correcting common quantum computing errors, which could pave the way for more reliable systems.
Researchers discover a new superfluid phase in non-Hermitian quantum systems
A stable "exceptional fermionic superfluid," a new quantum phase that intrinsically hosts singularities known as exceptional points, has been discovered by researchers at the Institute of Science Tokyo.
Simulating Driven-Dissipative Quantum Spin Dynamics On Consumer Hardware
Physics simulations using classical mechanics is something that’s fairly easily done on regular consumer hardware, with ...
Quantum science and technology: highlights of 2025
As the International Year of Quantum Science and Technology draws to a close, Margaret Harris revisits some of the year’s best work in this ever-popular field
Mysterious quantum spin liquids emerge from precisely grown kagome crystals
Neutrons are ideal probes because they can penetrate deep into the crystal and interact directly with the spins of electrons.
Evidence of a quantum spin liquid ground state in a kagome material
Quantum spin liquids are exotic states of matter in which spins (i.e., the intrinsic angular momentum of electrons) do not ...
Quantum computing works — now investors will see if the stocks do too
In December 2024, Google’s Willow chip became the first quantum processor to demonstrate “below threshold” error correction.
Physicists used 'dark photons' in an effort to rewrite physics in 2025
A new theory of "dark photons" attempted to explain a centuries-old experiment in a new way this year, in an effort to change our understanding of the nature of light
Quantum Computing vs. Classical Computing: What’s the Difference? 🤔
Your computer thinks in 0s and 1s. Quantum computers think in... everything at once. Here is the difference between Classical ...
Show HN: minfern – Type inference for JavaScript without transpilation
I wrote a type checker (minfern - https://github.com/sinelaw/minfern) for a subset of javascript with no transpilation. It accepts valid JS as input and allows a subset of JS code to pass the checking. There’s no transpilation or compilation needed. The input code can be run directly.Try it online at: https://sinelaw.github.io/minfern/It could be used for checking code targeting normal JS runtimes, or runtimes like mquickjs (though minfern is more strict b
Show HN: Aegis Memory v1.2 – We solved "what's worth remembering" for AI agents
Aegis Memory is an open-source, self-hostable memory layer for multi-agent AI systems.v1.2 adds Smart Memory - a two-stage pipeline that automatically decides what's worth storing:
1. Fast rule-based filter catches obvious noise (greetings, "thanks", etc.)
2. LLM extracts atomic facts only when the filter passesThis saves ~70% of extraction costs while keeping memory high-quality.Try it in 15 seconds:
pip install aegis-memory
aegis demoGitHub: https://github.com/
Is IonQ Stock a Buy After This 100-Qubit System Sale?
IonQ just sold a 100-qubit quantum system to South Korea. Here's why that's not enough to make me buy the stock.
Critical minerals are hiding in plain sight in U.S. Mines
Researchers found that U.S. metal mines already contain large amounts of critical minerals that are mostly going unused. Recovering even a small fraction of these byproducts could sharply reduce dependence on imports for materials essential to clean energy and advanced technology. In many cases, the value of these recovered minerals could exceed the value of the mines’ primary products. The findings point to a surprisingly simple way to boost domestic supply without opening new mines.
Most distant supernova: James Webb sees a star explode at cosmic dawn
Scientists have detected the most distant supernova ever seen, exploding when the universe was less than a billion years old. The event was first signaled by a gamma-ray burst and later confirmed using the James Webb Space Telescope, which was able to isolate the blast from its faint host galaxy. Surprisingly, the explosion closely resembles supernovae linked to gamma-ray bursts in the modern universe.
Fusion reactors may create dark matter particles
Researchers say fusion reactors might do more than generate clean energy—they could also create particles linked to dark matter. A new theoretical study shows how neutrons inside future fusion reactors could spark rare reactions that produce axions, particles long suspected to exist but never observed. The work revisits an idea teased years ago on The Big Bang Theory, where fictional physicists couldn’t solve the puzzle. This time, real scientists think they’ve found a way.
Scientist claims the universe is intelligent and your brain taps it
The idea that the universe itself might be intelligent sounds like science fiction, yet a growing group of researchers is ...
Show HN: Solving the H100 OOM Wall with CTDR – Maxwell Dashboard Included
I’ve conducted a technical audit on the physical limits of H100 GPUs for long-context retrieval. At scale (N=500k+), standard fp16 NxN materialization is physically impossible on a single 80GB card (requires ~500GB HBM).I’m releasing CTDR (Cold Tensor Deterministic Reasoning) evidence and the Maxwell Dashboard. It allows you to:
1. Visualize the OOM Feasibility Boundary (The NxN Wall).
2. Audit real NVML energy receipts (Joules per query) showing 90.4% SM utilization.
3. Compare your own GPU per
Julia vs. NumPy performance: Strategy for For-loop?
Lately, read about Julia Langs' preference in some quantum research. In general, for single shot computations like matrix and vector operations including linear algebra, the performance between Julia and NumPy is comparable due to NumPy's underlying C/Fortran code base. But for Monte Carlo methods for-loops are hard to avoid. Is there any strategy for For-loops in NumPy avoiding python overhead? Or migrating to Julia is a better choice? I am trying to avoid Rust as I see it as mo
Show HN: Apps by AI (Claude Opus 4.5)
I'm having Claude Opus 4.5 make as many HTML/JS apps as it can -- 100+ so far. Quite remarkable how capable it is, one-shotting most of these.