Image Compression and Classification Using Qubits and Quantum Deep Learning

Abstract

Recent work suggests that quantum machine learning techniques can be used for classical image classification by encoding the images in quantum states and using a quantum neural network for inference. However, such work has been restricted to very small input images, at most 4 x 4, that are unrealistic and cannot even be accurately labeled by humans. The primary difficulties in using larger input images is that hitherto-proposed encoding schemes necessitate more qubits than are physically realizable. We propose a framework to classify larger, realistic images using quantum systems. Our approach relies on a novel encoding mechanism that embeds images in quantum states while necessitating fewer qubits than prior work. Our framework is able to classify images that are larger than previously possible, up to 16 x 16 for the MNIST dataset on a personal laptop, and obtains accuracy comparable to classical neural networks with the same number of learnable parameters. We also propose a technique for further reducing the number of qubits needed to represent images that may result in an easier physical implementation at the expense of final performance. Our work enables quantum machine learning and classification on classical datasets of dimensions that were previously intractable by physically realizable quantum computers or classical simulation

References in corpus (16)

• Quantum Circuit Learning
• Characterizing Quantum Supremacy in Near-Term Devices
• Evaluating analytic gradients on quantum hardware
• Circuit-centric quantum classifiers
• Efficient Learning for Deep Quantum Neural Networks
• The quest for a Quantum Neural Network
• Quantum-enhanced machine learning
• Quantum generalisation of feedforward neural networks
• Quantum speedup for active learning agents
• Quantum computing and the entanglement frontier
• Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms
• Quantum learning and universal quantum matching machine
• Quantum Perceptron Models
• Single-step implementation of high fidelity $n$-bit Toffoli gate
• On the Origin of Implicit Regularization in Stochastic Gradient Descent
• Inductive supervised quantum learning