HN user
The explanation of Google's error correction experiment is basic but fine. People should keep in mind that Quantum Machines sells control electronics for quantum computers which is why they focus on the control and timing aspects of the experiment. I think a more general introduction to quantum error correction would be more relevant to the Hackernews audience.
This is not about AlphaQubit. It's about a different paper, https://arxiv.org/abs/2408.13687 and they do demonstrate real-time decoding.
we show that we can maintain below-threshold operation on the 72-qubit processor even when decoding in real time, meeting the strict timing requirements imposed by the processor’s fast 1.1 μs cycle duration
I've used DVC for most of my projects for the past five years. The good things is that it works a lot like git. If your scientists understand branches, commits and diffs, they should be able to understand DVC. The bad thing is that it works like git. Scientists often do not, in fact, understand or use branches, commits and diffs. The best thing is that it essentially forces you to follow Ten Simple Rules for Reproducible Computational Research [1]. Reproducibility has been a huge challenge on teams I've worked on.
[1] https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...
Quantum computers have not yet achieved any practical application. What they have achieved is performing highly specialized tasks like random circuit sampling or quantum simulation which would be too difficult for even the largest classical computers. While these tasks are highly tailored to the machines and effectively useless in the real world, they are exciting as they are demonstration that these devices can perform computations beyond classical. Turning this computing power towards practical use-cases is more challenging, but something we are likely to see in the next few years. The first applications will likely be ones like quantum chemistry where the problem is inherently quantum mechanical and thus maps more naturally to a quantum computer, or quantum-enhanced classical algorithms which lean heavily on classical solvers combined with quantum. The most important quantum algorithms require error-corrected qubits, which are some ways away but current devices are starting to demonstrate this at a small scale.
A few weeks ago with Ubuntu 22.04 on i7 with 16GB RAM. Perhaps it's related to using the devcontainer feature of VSCode, although I run jupyterlab in the same container
I have tried VSCode many times, but I find the performance of the notebook UI to be terrible. It seems to be popular, so maybe it's just me.
In any case, jupyterlab + jupyter-lsp gets most of the benefits for me.
I'm currently using Duolingo to learn Portguese. I've found it to be a very effective way to build up a basic vocabulary and grasp the pronunciation. I plan to start to in-person classes in a couple months once I know ~1000 words. It seems about as effective as anything else to me. In my experience learning a new language is almost entirely about how much effort you put in, rather than the method.
One thing that strikes me from these comments is that many people become focused on completing the exercises and getting points. I can see that could happen, it requires a conscious effort to make sure I understand every sentence and say it out loud.
I studied semiconductor engineering in Canada about 10 years ago (graduated in 2011). Career propsects were poor, a few jobs available in California. A career in software would have been far better in terms of pay and opportunities.