crux
superconductor
crux | superconductor | |
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1 | 2 | |
0 | 9 | |
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0.0 | 0.0 | |
over 2 years ago | over 6 years ago | |
Cython | C++ | |
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crux
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Ask HN: Should I publish my research code?
Been there, done that. I published my doctoral research code [1] so that others could inspect, verify, replicate, extend, etc. YMMV, but the feedback I received from other researchers ranged from neutral to surprisingly positive (e.g. people using it in ways that pleasantly surprised me). But let me expand on my own experiences while developing that software, trying to figure out how to replicate the then-current state of the art.
At the time there were two widely used software packages for phylogenetic inference, PAUP* [2] and MrBayes [3]. The source code for MrBayes was available, and although at the time I had some pretty strong criticisms of the code structure, it was immensely valuable to my research, and I remain very grateful to its author for sharing the code. In contrast the PAUP* source was not available, and I struggled immensely to replicate some of its algorithms. As a case in point, I needed to compute the natural log of the gamma function with similar precision, but there was no documentation for how PAUP* did this. I eventually discovered that the PAUP* author had shared some of the low-level code with another project. Based on comments in that code I pulled the original references from the 60s literature and solved these problems that had plagued me for months in a matter of days. Now, from what I could see in that shared PAUP* code, I suspect that the PAUP* code is of very high quality. But the author significantly reduced his scientific impact by keeping the source to himself.
[1]: https://github.com/canonware/crux
superconductor
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Ask HN: Should I publish my research code?
I published some of my Academic code like a tool for simulating superconducting circuits [1] or a tool to manage lab instruments for quantum computing (or other) experiments [2]. It's super niche but both tools have found users in other labs that even keep developing them (at least for [2]). And it's nice to look at your code after 10 years and realize how much you've grown as a programmer :)
[1]: https://github.com/adewes/superconductor
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100-GHz Single-Flux-Quantum Bit-Serial Adder Based on 10-KA/Cm2 Niobium Process
I designed such superconducting chips for my MSc thesis and had them manufactured by Hypres, one of the few foundries that offered such a process. Designing superconducting circuits is great fun, I even wrote my own circuit simulator (https://github.com/adewes/superconductor/) as there were few good options available.
RSFQ was quite hyped up in the nineties, Prof. Likharev at Stony Brook had a team working on RSFQ circuit elements to replace conventional semiconductor logic. At the time the achievable speed was fantastic as compared to regular circuits, (un)fortunately semiconductor processes kept evolving and today RSFQ is only interesting for some niche applications like fast microwave circuits (and even there HEMT transistors are often a better solution).
Also, getting circuits with more than 10.000 junctions to work was quite tricky as the fabrication processes weren't very reliable and transferring flux quanta is a bit more noisy than storing charges on an FET, so I'm doubtful whether we could even have large-scale RSFQ circuits without extensive error correction.
Well, it's still an amazingly fun and fascinating field, really hope we might see a revival of it one day (maybe if we get room-temperature superconductors).
What are some alternatives?
python-qubit-setup - All scripts for controlling the instruments and acquiring data in our qubit setup.
cd4-histone-paper-code - Public release of most of the data analysis code for Lamere et. al. 2016
3d-reorganization-prostate-canc
minion
pyview - pyview contains all reusable and generic classes and functions that I used in my qubit data acquisition setup during my PhD thesis.
vole - A GAP package for backtrack search in permutation groups with graphs
3d-reorganization-prostate-cancer - Code, analysis, and results for Hawley, Zhou, et al., Cancer Research, 2021.