What big gap are you referring to that you believe exists between the theory of any quantum computing platform (which is device physics) and the experiment?
You seem to be conflating the theory with pitches to investors?
The number of qubits is increasing exponentially, and the error rates are getting lower.
People have factored numbers larger than 21 (not that Shor's algorithm is commonly used benchmarks by experimentalists at this point but people with little knowledge about quantum computers and device physics love it, https://link.springer.com/chapter/10.1007/978-3-032-12983-3_... did 221 and and in fact, you can do it yourself using Qiskit on IBM's publicly available devices [or on a local simulator for few qubits] following their tutorial https://qiskit.qotlabs.org/docs/tutorials/shors-algorithm if memory serves the largest instance for public is ibm_kingston with 156 qubits https://quantum.cloud.ibm.com/computers?limit=25&system=ibm_...) but it will take more time until we have millions of good qubits to harvest your Satoshis.
For the programmer folks here, as a physicists working on the device side of things for many years now, the best analogy I have is: we didn't get from a few hand-made vacuum tubes to billions of transistors with 18A manufacturing process overnight, and we won't get from hundreds to millions of better qubits overnight either. A realistic expectation would be thousands within this decade, but keep in mind that the growth has so far been exponential in various types of qubits, much like Moore's law, so reaching to millions of qubits shouldn't take us 10 millenia.
If what determines the value of a language libraries (which makes no sense to me at all, but let's play your game), then it is one more argument against Python.
You don't need FFI to use a Fortran library from Fortran, and I (and many physicists) have found Fortran better suited to HPC than Python since... the day Python came to existence. And no, many other scripting languages have wrappers, and no, scientific computing is not restricted to ML which the only area Python can be argued to have most wrapper libraries to external code.
Language matters, and two-language problem is a real problem, and you can't make it go away by closing your ears and chanting "doesn't matter! doesn't matter!"
Julia is a real step toward solving this problem, and allows you to interact with libraries/packages in ways that is not possible in Python + Fortran + C/C++ + others. You are free to keep pretending that problem doesn't exist.
You are making disparaging and hyperbolic claims about hyperbolic claims without proper attribution, and when asked for source, you cry foul and sadly try to appear smart by saying "you're acting dumb". You should take on your advice and instead of "acting dumb", explicitly cite what "promises" or "bombastic claims" you are referring to. This is what I asked you to do, but instead of doing it, you are doing what you are doing, which is interesting.
Since you have a rosy picture of Python, I assume you're young. Python has been mostly a fringe/toy language for 2 decades, until around ~2010, when a Python fad started not too different from the Rust fad of today, and at some point Google started using it seriously and thought they can fix Python but gave up eventually. The fad lived on and kept evolving and somehow found some popularity with SciPy and then ML. I used it in 90s a little, and I found the language bad for anything other than replacing simple bash scripts or simple desktop applications or a desktop calculator, and I still think it is (but sure, there are people who disagree and think it is a good language). It was slow and didn't have type system, you didn't know whether your code would crash or not until you run that line of code, and the correctness of your program depended on invisible characters.
"Ecosystem" is not a part of the language, and in any case, the Python ecosystem is not written in Python, because Python is not a suitable language for scientific computing, which is unsurprising because that's not what it was designed for.
It is ironic you bring up hype to criticize Julia while praising Python which found popularity thanks to hype rather than technical merit.
What promise are you referring to? Who promised you what? It's a programming language.
Telling what? Did you actually listen to the talk that you linked to, or read the top comment there by Chris Rackauckas?
> Given all that, outside of depending heavily on DifferentialEquations.jl, I don't know why someone would pick Julia over Python + Rust.
See his last slide. And no, they didn't replace their Julia use in its entirety with Rust, despite his organization being a Rust shop. Considering Rust as a replacement for Julia makes as much sense to me as to considering C as a replacement for Mathematica; Julia and Mathematica are domain specific (scientific computation) languages, not general systems programming languages.
Neither Julia nor Mathematica is a good fit for embedded device programming.
I also find it amusing how you criticize Julia while praising Python (which was originally a "toy" scripting language succeeding ABC, but found some accidental "gaps" to fit in historically) within the narrative that you built.
> In any non-toy Julia program that's not going to be the case.
You seem to be conflating the theory with pitches to investors?
The number of qubits is increasing exponentially, and the error rates are getting lower. People have factored numbers larger than 21 (not that Shor's algorithm is commonly used benchmarks by experimentalists at this point but people with little knowledge about quantum computers and device physics love it, https://link.springer.com/chapter/10.1007/978-3-032-12983-3_... did 221 and and in fact, you can do it yourself using Qiskit on IBM's publicly available devices [or on a local simulator for few qubits] following their tutorial https://qiskit.qotlabs.org/docs/tutorials/shors-algorithm if memory serves the largest instance for public is ibm_kingston with 156 qubits https://quantum.cloud.ibm.com/computers?limit=25&system=ibm_...) but it will take more time until we have millions of good qubits to harvest your Satoshis.
For the programmer folks here, as a physicists working on the device side of things for many years now, the best analogy I have is: we didn't get from a few hand-made vacuum tubes to billions of transistors with 18A manufacturing process overnight, and we won't get from hundreds to millions of better qubits overnight either. A realistic expectation would be thousands within this decade, but keep in mind that the growth has so far been exponential in various types of qubits, much like Moore's law, so reaching to millions of qubits shouldn't take us 10 millenia.