Learning and Inference on Generative Adversarial Quantum Circuits

Understand

Quantum mechanics is inherently probabilistic in light of Born's rule.

• Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability.
• However, training of quantum circuits can be more challenging compared to classical neural networks due to lack of efficient differentiable learning algorithm.
• We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together.