Classical-to-quantum convolutional neural network transfer learning

Abstract

Machine learning using quantum convolutional neural networks (QCNNs) has demonstrated success in both quantum and classical data classification. In previous studies, QCNNs attained a higher classification accuracy than their classical counterparts under the same training conditions in the few-parameter regime. However, the general performance of large-scale quantum models is difficult to examine because of the limited size of quantum circuits, which can be reliably implemented in the near future. We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent. In the classical-to-quantum transfer learning framework, a QCNN can solve complex classification problems without requiring a large-scale quantum circuit by utilizing a pre-trained classical convolutional neural network (CNN). We perform numerical simulations of QCNN models with various sets of quantum convolution and pooling operations for MNIST data classification under transfer learning, in which a classical CNN is trained with Fashion-MNIST data. The results show that transfer learning from classical to quantum CNN performs considerably better than purely classical transfer learning models under similar training conditions.

arXiv comment16 pages, 7 figures

References in corpus (30)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Quantum Computing in the NISQ era and beyond
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• A variational eigenvalue solver on a quantum processor
• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Parameterized quantum circuits as machine learning models
• Evaluating analytic gradients on quantum hardware
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• Quantum autoencoders for efficient compression of quantum data
• Optimal Quantum Circuits for General Two-Qubit Gates
• Transfer learning in hybrid classical-quantum neural networks
• Quantum convolutional neural network for classical data classification
• Quantum-state preparation with universal gate decompositions
• Robust data encodings for quantum classifiers
• Hierarchical quantum classifiers
• Quantum Computation of Electronic Transitions using a Variational Quantum Eigensolver
• Hybrid Quantum-Classical Approach to Quantum Optimal Control
• A divide-and-conquer algorithm for quantum state preparation
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• Efficient Scheme for Initializing a Quantum Register with an Arbitrary Superposed State
• Quantum computing models for artificial neural networks
• Circuit-based quantum random access memory for classical data with continuous amplitudes

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• Quantum Convolutional Neural Networks are Effectively Classically Simulable
• Quantum Transfer Learning with Adversarial Robustness for Classification of High-Resolution Image Datasets
• Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning
• Optimizing Quantum Convolutional Neural Network Architectures for Arbitrary Data Dimension
• Quantum support vector data description for anomaly detection
• ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks
• Expressivity of deterministic quantum computation with one qubit
• Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer