Hybrid Classical-Quantum Machine Learning for Image Recognition on CIFAR-10

Even with the limitations of current noisy intermediate scale quantum (NISQ) devices, hybrid classical-quantum machine learning implementations have been demonstrated on both NISQ hardware and in simulation performing image classification. Building on previous work, input images' latent representations, coming from a classical neural network such as EfficientNet, are processed by a quantum circuit, whose measured outputs are then used by a classical network to classify input images. Improvements to prior hybrid methods are implemented and the resultant model trained and evaluated on the CIFAR-10 standard computer vision dataset. We present an overview of the theory behind these hybrid approaches, the improvements made to them, and a comparison of the results achieved from those improvements to top classical algorithms applied to the same data.