Re-QGAN: an optimized adversarial quantum circuit learning framework
Adversarial learning represents a powerful technique for generating data statistics. Its successful implementation in quantum computational platforms is not straightforward due to limitations in connectivity, quantum operation fidelity, and limited access to the quantum processor for statistically relevant results. Constraining the number of quantum operations and providing a design with a low compilation cost, this code creates a quantum generative adversarial network design that uses real Hilbert spaces as the framework for the generative model and a novel strategy to encode classical information into the quantum framework. For more information check “Re-QGAN: an optimized adversarial quantum circuit learning framework” ArXiV Link
