Generation of High Resolution Handwritten Digits with Samples from a Quantum Device

We present the first practical and experimental implementation of a quantum-classical generative algorithm capable of generating high-resolution images of handwritten digits with quantum samples from an ion-trap quantum device. In our scheme, we take advantage of a recently proposed quantum generative framework known as the Quantum Circuit Born Machine (QCBM) to model and sample the prior distribution of an Associative Adversarial Network; the latter being an extension of the widely-used Generative Adversarial Networks (GANs). To maximize the potential of this algorithm on NISQ devices, we propose a novel technique that leverages on the unique quantum possibilities of measuring in bases other than the computational basis, enhancing the expressibility of the prior distribution of our quantum-classical approach. A fully-connected classical neural network layer is trained to extract maximal information of the measurements unlocked by the basis-enhanced QCBM model. We present experimental realization of a full training on an ion-trap device and use the algorithm to generate high-quality images and quantitatively outperform comparable classical GANs trained on the MNIST data set for handwritten digits.