Quantum Curriculum Learning
This addresses resource optimization for quantum machine learning on noisy intermediate-scale quantum devices, but it is incremental as it adapts an existing curriculum learning strategy to quantum data.
The paper tackles the problem of high quantum resource requirements and training complexity in quantum machine learning by proposing quantum curriculum learning (Q-CurL), which uses simpler tasks first to improve convergence and generalization, achieving significant enhancements in unitary learning and quantum phase recognition tasks.
Quantum machine learning (QML) requires significant quantum resources to address practical real-world problems. When the underlying quantum information exhibits hierarchical structures in the data, limitations persist in training complexity and generalization. Research should prioritize both the efficient design of quantum architectures and the development of learning strategies to optimize resource usage. We propose a framework called quantum curriculum learning (Q-CurL) for quantum data, where the curriculum introduces simpler tasks or data to the learning model before progressing to more challenging ones. Q-CurL exhibits robustness to noise and data limitations, which is particularly relevant for current and near-term noisy intermediate-scale quantum devices. We achieve this through a curriculum design based on quantum data density ratios and a dynamic learning schedule that prioritizes the most informative quantum data. Empirical evidence shows that Q-CurL significantly enhances training convergence and generalization for unitary learning and improves the robustness of quantum phase recognition tasks. Q-CurL is effective with physical learning applications in physics and quantum chemistry.