Quantum machine learning for image classification

Abstract

Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

13 pages, 10 figures, 1 table

References in corpus (23)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Quantum Computing
• An introduction to quantum machine learning
• Hybrid quantum neural network for drug response prediction
• Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
• Quantum Machine Learning: from physics to software engineering
• Quantum Methods for Neural Networks and Application to Medical Image Classification
• Deep learning in biomedical optics
• Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning
• Quantum algorithms applied to satellite mission planning for Earth observation
• Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
• Hybrid quantum ResNet for car classification and its hyperparameter optimization
• ZX-calculus for the working quantum computer scientist
• Parallel Hybrid Networks: an interplay between quantum and classical neural networks
• Efficient calculation of gradients in classical simulations of variational quantum algorithms
• Practical application-specific advantage through hybrid quantum computing
• An exponentially-growing family of universal quantum circuits
• A supervised hybrid quantum machine learning solution to the emergency escape routing problem
• Hybrid Quantum Generative Adversarial Networks for Molecular Simulation and Drug Discovery
• Variational Quantum Neural Networks (VQNNS) in Image Classification
• Completeness of the ZX-calculus
• Hybrid quantum-classical convolutional neural networks to improve molecular protein binding affinity predictions
• Scalable Quantum Convolutional Neural Networks

Cited by in corpus (20)

• Quantum algorithms applied to satellite mission planning for Earth observation
• Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
• Hybrid quantum cycle generative adversarial network for small molecule generation
• Harnessing Quantum Extreme Learning Machines for image classification
• An exponentially-growing family of universal quantum circuits
• Quantum autoencoders for image classification
• Forecasting steam mass flow in power plants using the parallel hybrid network
• Satellite image classification with neural quantum kernels
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Quantum kernel learning Model constructed with small data
• Classification and reconstruction for single-pixel imaging with classical and quantum neural networks
• QuFeX: Quantum feature extraction module for hybrid quantum-classical deep neural networks
• Quantum optical classifier with superexponential speedup
• Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
• Lean classical-quantum hybrid neural network model for image classification
• A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
• Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
• Quantum Encoding of Structured Data with Matrix Product States
• Multivariate unbounded quantum regression via log-ratio probabilities mitigating barren plateaus
• Quantum feature-map learning with reduced resource overhead