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hardware-aware-ml

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christopher-altman / sqnt-hardware-demo

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Adaptive quantum networks in practice: superposed graph topologies and operator-space spatialization, with reproducible hardware-relevant demos and figures.

reproducible-research numpy inverse-problems identifiability quantum-machine-learning qiskit graph-neural-networks quantum-neural-networks mixture-models pennylane quantum-compilation adaptive-quantum-networks superposed-graph-topologies superpositional-topologies topology-learning model-identifiability support-recovery operator-space deterministic-experiments hardware-aware-ml
  • Updated Jan 19, 2026
  • Python

or4k2l / robust-vision

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Production-ready framework for training robust computer vision models. Features multi-GPU support, EMA tracking, label smoothing, and comprehensive robustness evaluation across 4 noise types. Includes scalable TF.Data pipeline, automated testing, Docker support, and CLI tools. Install: pip install robust-vision

python docker machine-learning computer-vision deep-learning pattern-recognition autonomous-driving kitti-dataset multi-gpu neuromorphic-hardware neuromorphic-computing memristive-crossbar jax confidence-estimation production-ml noise-robustness implicit-regularization robust-ml hardware-aware-ml
  • Updated Feb 9, 2026
  • Python

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