Hybrid quantum-classical schemes for generative adversarial learning: HQGANs

Quantum computing and machine learning are two fast-growing research areas. Recently, these two areas have been merged into the field of quantum machine learning (QML), seeking to find ways in which quantum computers can offer advantages at solving machine learning problems over classical computers. Here, we propose to use quantum computers to learn models that mimic observed data distributions, a type of task known as generative learning, by substituting neural networks with variational quantum circuits in the generative adversarial networks (GANs) framework. GANs are statistical models that learn to sample from an observed data distribution by looking at individual samples. They consist of two neural networks, known as the discriminator and the generator, competing against each other in a minimax game. We propose a hybrid-quantum classical scheme that trains two variational quantum circuits, playing the role of discriminator and generator, to perform the same task on classical data. The proposed hybrid quantum GANs (HQGANs) might benefit machine learning by improving the ability to model more complex data distributions and could offer a new niche of applications for near-term quantum computers.