Learning and Inference on Generative Adversarial Quantum Circuits
This work proposes a novel approach for machine learning on near-term quantum devices, potentially offering quantum advantage for data generation and inference tasks.
The paper tackles the challenge of training quantum circuits as generative models by introducing a hybrid quantum-classical adversarial training scheme, achieving quadratic speedup in inference via amplitude amplification.
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. After training, the quantum circuit generative model can infer missing data with quadratic speed up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuit using the prototypical Bars-and-Stripes dataset. Generative adversarial quantum circuits is a fresh approach to machine learning which may enjoy the practically useful quantum advantage on near-term quantum devices.