No description available

Cirq/PyTorch implementation of Quantum Architecture Search via Deep Reinforcement Learning by (Kuo et al., 2021)

main

RL-based Neural Architecture Search for Quantum Neural Networks

Gradient-based quantum neural architecture search for NISQ-compatible medical imaging. Jointly optimizes circuit topology and weights under hardware constraints — 78.6% fewer parameters, 31.5% gate reduction, 98.9% noise retention vs. 90.9% classical. 15× faster than evolutionary QML-NAS.

The code for BQNAS

Quantum Pre-Resolution Search Architecture Using Contextual Superposition States for Intent Prediction — a defensive publication hosted by Unpatentable.org.