I just published a fun interactive 3D demo of SPDC, one of the most common and accessible ways to create entangled pairs of photons. I'm hoping to publish a series of articles on other cool learnings about doing quantum photonics in the lab.

https://paulg.info/2026/04/10/spdc/

Probably the coolest part has been automating the optomechanical equipment and optimizing physical experiments with Bayesian optimization. Similar to hyperparameter tuning in ML, but with lasers.

Also, Thorlabs sells some really fun toys.

I solved this problem with a twilio sms number. When I send a text to it, the content gets prepended to my obsidian todo.md file. This was easy to arrange with a few lines of Python glue.

iOS makes it easy to text or share to sms from almost any context.

I have read numerous explanations, but haven't found a really authoritative discussion.

[0] https://en.wikipedia.org/wiki/Leap_second#Rationale

https://www.cambridge.org/core/books/decoherence-and-quantum...

As well, any files or symbols mentioned by the model are noted. They influence the repomap ranking algorithm, so subsequent requests have even more relevant repository context.

This is designed as a sort of implicit search and ranking flow. The blog article doesn’t get into any of this detail, but much of this has been around and working well since 2023.

https://www.cleardarksky.com/csk/

One aspect that HN may find interesting is my use of Bayesian optimization to control and perfect key experimental settings. About a dozen of the wave plates and other optical components are motorized and under computer control.

Given a goal metric like "maximally entangle the photon pairs" the optimizer will run the experiment 50-100 times, tweaking the angles of various optics and collecting data. Ultimately it will learn to maximize the given cost function.

This sort of thing is commonly done with tools like Optuna during NN/LLM training to optimize hyper-parameters, but seems less common in physics especially quantum photonics. I'm using a great tool called M-loop to drive the optimization, which was originally developed for creating Bose-Einstein condensates.

https://github.com/michaelhush/M-LOOP

1. We've raised CO2 from 280ppm to 420ppm, about a 50% increase. To dilute it back down would require 50% more total atmosphere. This would also raise the surface air pressure 1.5x.

2. How much heat is trapped is related to the absolute amount of CO2 in the atmosphere, not the fraction. So the diluted atmosphere would retain just as much heat.

Einstein’s paper “On the Electrodynamics of Moving Bodies” with special relativity was published in 1905. His work on general relativity was published 10 years later in 1915. The earliest knowledge cuttoff of these models is 1913, in between the relativity papers.

The knowledge cutoffs are also right in the middle of the early days of quantum mechanics, as various idiosyncratic experimental results were being rolled up into a coherent theory.

My solution is a twilio text number that automatically inserts any texts it receives into the top of my todo.md file. Previously todo.org, until about a year ago.

iOS has ubiquitous support to quickly share to SMS from any/everywhere. It’s easy to send a text to this contact from a Home Screen shortcut, but also also from the share sheet in most every app.

1. Minimize the number of PDF pages per context/call. Don't dump a giant document set into one request. Break them into the smallest coherent chunks.

2. In a clean context, re-send the page and the extracted target content and ask the model to proofread/double-check the extracted data.

3. Repeat the extraction and/or the proofreading steps with a different model and compare the results.

4. Iterate until the proofreadings pass without altering the data, or flag proofreading failures for stronger models or human intervention.

[0] https://github.com/paul-gauthier/entangled-pair-quantum-eras...

The code is also a useful artifact that can be iteratively edited and improved by both the human and LLM, with git history, etc. Running and passing tests/assertions helps to build and maintain confidence that the math remains correct.

I use helper functions to easily render from the sympy code to latex, etc.

A lot of the math behind this quantum eraser experiment was done this way.

https://github.com/paul-gauthier/entangled-pair-quantum-eras...

https://youtu.be/FbOXRuBQt_U

https://youtu.be/HvJeXakc8Kc

https://www.howtoinventeverything.com/

https://scottaaronson.blog/?p=3975