artiq
acados
| artiq | acados | |
|---|---|---|
| 2 | 5 | |
| 403 | 677 | |
| 1.0% | 2.5% | |
| 9.6 | 8.9 | |
| 8 days ago | 2 days ago | |
| Python | C | |
| GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
artiq
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Senior FPGA Engineer in quantum computing startup, Oxfordshire UK
At Oxford Ionics we're looking for a senior FPGA engineer to work on our ARTIQ-based experimental control system and build our FPGA team. We're using Migen HDL and Python and software engineering knowledge are highly desirable. No prior quantum computing knowledge is required!
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
No, I mean nanosecond and picosecond precision real-time systems. Exhibit A: https://github.com/m-labs/artiq
acados
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How to understand Model Predictive Control
I would check out CasADi (specifically the opti framework) and or ACADOS. To code up a quick MPC in general is not hard, but to squeeze efficiency and exploit sparsity for good real-time performance is a little more involved and these tools really help with that.
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Question about Model Predictive Control (MPC) cost function
Generally, nonlinear MPC uses either IPOPT (an interior point method) or sequential quadtraic programming based approaches (google GURBOI, qpoases, qrqp...). A good python framework is CasADi, or its sister project ACADOS. I think there is also a fair amount of literature on learning MPC cost functions from data you could probably find.
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Show HN: prometeo – a Python-to-C transpiler for high-performance computing
Thanks for the question! My background is in numerical optimization for optimal control. Projects like this https://github.com/acados/acados motivated the development of prometeo. It's mostly about solving optimization problems as fast as possible to make optimal decisions in real-time.
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Do you know a good free toolbox on mpc control for GNU Octave?
Look at Acados. I didn't use it with Octave, but according the readme it has a interface with Octave.
What are some alternatives?
quantumcat - quantumcat is a platform-independent, open-source, high-level quantum computing library, which allows the quantum community to focus on developing platform-independent quantum applications without much effort.
StaticCompiler.jl - Compiles Julia code to a standalone library (experimental)
py2many - Transpiler of Python to many other languages
pyomo - An object-oriented algebraic modeling language in Python for structured optimization problems.
PhysAI - PhysAI is an open-source AI project that aims to link quantum mechanics and general relativity by generating, testing, and improving physical equations. It leverages machine learning, integrates with existing research, generates LaTeX documents, and encourages collaborative learning. It relies on community-driven contributions to improve accuracy.
Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
cqasm_development_interface - Framework for writing and running cQASM files against any Quantum Inspire's emulator backend via their API
Metatheory.jl - General purpose algebraic metaprogramming and symbolic computation library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.
qutrunk - QuTrunk is free, open source, cross platform quantum computing programming framework, including quantum programming API, quantum command translation, quantum computing back-end interface, etc
hpipm - High-performance interior-point-method QP and QCQP solvers