Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images

Abstract

We compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the task of supervised binary image classification. We keep the employed quantum circuits compatible with near-term quantum devices and use two distinct methodologies: applying randomized NNs on dimensionality-reduced data and applying CNNs to full image data. We evaluate these approaches on three fully-classical data sets of increasing complexity: an artificial hypercube data set, MNIST handwritten digits and industrial images. Our central goal is to shed more light on how quantum and classical models perform for various binary classification tasks and on what defines a good quantum model. Our study involves a correlation analysis between classification accuracy and quantum model hyperparameters, and an analysis on the role of entanglement in quantum models, as well as on the impact of initial training parameters. We find classical and quantum-classical hybrid models achieve statistically-equivalent classification accuracies across most data sets with no approach consistently outperforming the other. Interestingly, we observe that quantum NNs show lower variance with respect to initial training parameters and that the role of entanglement is nuanced. While incorporating entangling gates seems advantageous, we also observe the (optimizable) entangling power not to be correlated with model performance. We also observe an inverse proportionality between the number of entangling gates and the average gate entangling power. Our study provides an industry perspective on quantum machine learning for binary image classification tasks, highlighting both limitations and potential avenues for further research in quantum circuit design, entanglement utilization, and model transferability across varied applications.

26 pages, 12 figures

References in corpus (39)

• Quantum Machine Learning
• Variational Quantum Algorithms
• Deep Learning using Rectified Linear Units (ReLU)
• Barren plateaus in quantum neural network training landscapes
• Quantum support vector machine for big data classification
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• An introduction to quantum machine learning
• Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
• Circuit-centric quantum classifiers
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Power of data in quantum machine learning
• Data re-uploading for a universal quantum classifier
• Quantum advantage in learning from experiments
• A rigorous and robust quantum speed-up in supervised machine learning
• Quantum algorithms for supervised and unsupervised machine learning
• An initialization strategy for addressing barren plateaus in parametrized quantum circuits
• Hierarchical quantum classifiers
• The Expressive Power of Parameterized Quantum Circuits
• Information-theoretic bounds on quantum advantage in machine learning
• Implementing a distance-based classifier with a quantum interference circuit
• Is quantum advantage the right goal for quantum machine learning?
• Demonstration of quantum advantage in machine learning
• Quantum machine learning for image classification
• Training Quantum Embedding Kernels on Near-Term Quantum Computers
• Entanglement Devised Barren Plateau Mitigation
• Shadows of quantum machine learning
• Quantum classification of the MNIST dataset with Slow Feature Analysis
• Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits
• Let Quantum Neural Networks Choose Their Own Frequencies
• Reduction of finite sampling noise in quantum neural networks
• Quantum Convolutional Neural Networks are Effectively Classically Simulable
• Training robust and generalizable quantum models
• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits
• Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms
• Dynamic parameterized quantum circuits: expressive and barren-plateau free
• sQUlearn -- A Python Library for Quantum Machine Learning
• Do Quantum Neural Networks have Simplicity Bias?