Channel Attention for Quantum Convolutional Neural Networks
arXiv:2311.02871 · doi:10.1103/PhysRevA.110.012447
Abstract
Quantum convolutional neural networks (QCNNs) have gathered attention as one of the most promising algorithms for quantum machine learning. Reduction in the cost of training as well as improvement in performance is required for practical implementation of these models. In this study, we propose a channel attention mechanism for QCNNs and show the effectiveness of this approach for quantum phase classification problems. Our attention mechanism creates multiple channels of output state based on measurement of quantum bits. This simple approach improves the performance of QCNNs and outperforms a conventional approach using feedforward neural networks as the additional post-processing.
arXiv comment6 pages, 4 figures, 1 table
References in corpus (26)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Variational Quantum Algorithms
- Barren plateaus in quantum neural network training landscapes
- Quantum Circuit Learning
- Quantum Convolutional Neural Networks
- Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
- Circuit-centric quantum classifiers
- Quantum optimization using variational algorithms on near-term quantum devices
- Continuous-variable quantum neural networks
- Bloch vectors for qudits
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Beyond Barren Plateaus: Quantum Variational Algorithms Are Swamped With Traps
- One-Dimensional Symmetry Protected Topological Phases and their Transitions
- Challenges and Opportunities of Near-Term Quantum Computing Systems
- Topology and edge modes in quantum critical chains
- Application-Oriented Performance Benchmarks for Quantum Computing
- Statistical mechanics of the Cluster-Ising model
- Crossing a topological phase transition with a quantum computer
- Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases
- Barren plateaus in quantum tensor network optimization
- Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
- Machine Learning Phase Transitions with a Quantum Processor
- Quantum phase detection generalisation from marginal quantum neural network models
- Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
- Testing platform-independent quantum error mitigation on noisy quantum computers
- Randomness-enhanced expressivity of quantum neural networks