Tootfinder

Quantum computers must be cooled to extremely low temperatures to minimize vibrations and prevent errors.
So far, chip-based systems have been limited to inefficient and slow cooling methods.
Now, a team of researchers at MIT and MIT Lincoln Laboratory has implemented a much faster and more energy-efficient method for cooling photonic chip-based quantum computers.
Their approach achieved cooling to about 10 times below the limit of standard laser cooling.
Key to this te…

Efficient cooling method could enable chip-based trapped-ion quantum computers
Researchers demonstrated a new method of cooling trapped ions using chip-based systems, which could enable more stable and scalable quantum computers and quantum sensors.

In my new award-winning series called "Things My Homelab Minirack Needs": #homelab

Mathematical basis, phase transitions and singularities of (3 1)-dimensional phi4 scalar field model
Zhidong Zhang
https://arxiv.org/abs/2511.07439 https://arxiv.org/pdf/2511.07439 https://arxiv.org/html/2511.07439
arXiv:2511.07439v1 Announce Type: new
Abstract: The lambda phi4 scalar field model that can be applied to interpret pion-pion scattering and properties of hadrons. In this work, the mathematical basis, phase transitions and singularities of a (3 1)-dimensional (i.e., (3 1)D) phi4 scalar field model are investigated. It is found that as a specific example of topological quantum field theories, the (3 1)D phi4 scalar field model must be set up on the Jordan-von Neumann-Wigner framework and dealt with the parameter space of complex time (or complex temperature). The use of the time average and the topologic Lorentz transformation representing Reidemeister moves ensure the integrability, which takes into account for the contributions of nontrivial topological structures to physical properties of the many-body interacting system. The ergodic hypothesis is violated at finite temperatures in the (3 1)D phi4 scalar field model. Because the quantum field theories with ultraviolet cutoff can be mapped to the models in statistical mechanics, the (3 1)D phi4 scalar field model with ultraviolet cutoff is studied by inspecting its relation with the three-dimensional (3D) Ising model. Furthermore, the direct relation between the coupling K in the 3D Ising model and the bare coupling lambda0 in the (3 1)D phi4 scalar field model is determined in the strong coupling limit. The results obtained in the present work can be utilized to investigate thermodynamic physical properties and critical phenomena of quantum (scalar) field theories.
toXiv_bot_toot

So AI Datacenters are going to space...
https://research.google/blog/exploring

Exploring a space-based, scalable AI infrastructure system design

Oh this really was fun, Scalar's interactive API docs on a FastAPI deployment is making me wonder if we should just use this for the workshop rather than trying to switch people over to use the Postman collection I made (also it's an Enterprise audience, and I already know that some of my attendees are not allowed to use Postman).

I’ve seen a lot of bullshit about “AI” but this might just be be the bullshittiest thing yet
https://research.google/blog/exploring-a-space-based-scalable-ai-infrastructure-system-design/

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner

Crosslisted article(s) found for physics.atom-ph. https://arxiv.org/list/physics.atom-ph/new
[1/1]:
- Searching for screened scalar forces with long-baseline atom interferometers
Hannah Banks, John Carlton, Benjamin Elder, Thomas Hird, Christopher McCabe

I enjoy LLMs the most when they are augmenting my existing skillset. I've lost count of how many demo APIs I've built with FastAPI for workshops but Claude built this one, configured it for deployment to Railway (a first for me), wrote the mildly amusing sample data I wanted, and switched to Scalar for docs with no fuss.
While I carry on with content I actually wanted to cover without getting bogged down in the details of the demo that's only there to let the attendees try some skills!

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- Differentiable, Bit-shifting, and Scalable Quantization without training neural network from scratch
Zia Badar