Image classification with quantum pre-training and auto-encoders

Abstract

Computer vision has a wide range of applications from medical image analysis to robotics. Over the past few years, the field has been transformed by machine learning and stands to benefit from potential advances in quantum computing. The main challenge for processing images on current and near-term quantum devices is the size of the data such devices can process. Images can be large, multidimensional and have multiple color channels. Current machine learning approaches to computer vision that exploit quantum resources require a significant amount of manual pre-processing of the images in order to be able to fit them onto the device. This paper proposes a framework to address the problem of processing large scale data on small quantum devices. This framework does not require any dataset-specific processing or information and works on large, grayscale and RGB images. Furthermore, it is capable of scaling to larger quantum hardware architectures as they become available. In the proposed approach, a classical autoencoder is trained to compress the image data to a size that can be loaded onto a quantum device. Then, a Restricted Boltzmann Machine (RBM) is trained on the D-Wave device using the compressed data, and the weights from the RBM are then used to initialize a neural network for image classification. Results are demonstrated on two MNIST datasets and two medical imaging datasets.

Transfer learning in hybrid classical-quantum neural networks

“…This hybrid approach is very convenient for processing high-resolution images since, in this configuration, a quantum computer is applied only to a fairly limited number of abstract features, which is much more feasible compared to embedding millions of raw pixels in a quantum system. We would like to mention that also other alternative approaches for dealing with large images have been recently proposed [21,29,35,42]. We applied our model for the task of image classification in several numerical examples and we also tested the algorithm with two real quantum computers provided by IBM and Rigetti.…”

“…Up to now, the transfer learning approach has been largely unexplored in the quantum domain with the exception of a few interesting applications, for example, in modeling many-body quantum systems [12,23,52], in the connection of a classical autoencoder to a quantum Boltzmann machine [35] and in the initialization of variational quantum networks [49]. With the present work we aim at developing a more general and systematic theory, specifically tailored to the emerging paradigms of variational quantum circuits and hybrid neural networks.…”

Transfer learning in hybrid classical-quantum neural networks

“…This hybrid approach is very convenient for processing highresolution images since, in this configuration, a quantum computer is applied only to a fairly limited number of abstract features, which is much more feasible compared to embedding millions of raw pixels in a quantum system. We would like to mention that also other alternative approaches for dealing with large images have been recently proposed [28,[35][36][37].…”

“…Up to now, the transfer learning approach has been largely unexplored in the quantum domain with the exception of a few interesting applications, for example, in modeling many-body quantum systems [25][26][27], in the connection of a classical autoencoder to a quantum Boltz-mann machine [28] and in the initialization of variational quantum networks [29]. With the present work we aim at developing a more general and systematic theory, specifically tailored to the emerging paradigms of variational quantum circuits and hybrid neural networks.…”

COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

“…Zen et al [26] used transfer learning for the scalability of neural network quantum states. Piat et al [27] studied image classification with quantum pre-training and auto-encoders. Verdon et al [28] examined learning to learn with quantum neural networks via classical neural networks.…”