Simulate optical communications systems with Python.
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Updated
May 14, 2026 - Python
Simulate optical communications systems with Python.
Belief propagation with sparse matrices (scipy.sparse) in Python for LDPC codes. Includes NumPy implementation of message passing (min-sum and sum-product) and a few other decoders.
My implementation of LDPC codes. For details visit: https://yairmz.github.io/LDPC/
A fully verified, 3GPP NR compliant 5G physical layer library for Python. Includes high-PHY and low-PHY implementations, validated against 60,000+ test cases generated via the MATLAB 5G Toolbox.
Python Tools for Quantum Error Correction
Belief propagation on Tanner graphs (LDPC decoder)
QEC is a deterministic, replay-safe proof and systems architecture for quantum error correction, invariant-driven computation, canonical hashing, replay validation, semantic/citation boundary enforcement, proof telemetry, inference and agent-boundary receipts, research provenance, and hash-bound decoder governance.
Frontier decoder for quantum LDPC codes: pruned dynamic programming over boundary states for approximate logical maximum-likelihood decoding of sparse parity-check and detector-error-model matrices.
Python implementation and simulation of LDPC error-correcting codes with BER analysis and parallel decoding
Master thesis
Tools for permutation and interleaver generation.
Reproducibility code for 'Achievable Rates of Coded NOMA with Finite-Alphabet Inputs and Suboptimal Detection' (IEEE Trans. Commun., 2026)
Fast Python-only LDPC BP decoder, demoed using a mixed-degree (check-node degs 6&7) code
Runnable, tested Python implementations of the core algorithms from David MacKay's Information Theory, Inference, and Learning Algorithms (ITILA): Huffman & arithmetic coding, Hamming/(7,4) codes, BSC capacity, LDPC sum-product decoding, MCMC, and EM.
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