Quantum Curriculum Learning

Abstract

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.

Updated with schematic figures of quantum circuits and transparent explanation for Curriculum Learning

References in corpus (24)

• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Evaluating analytic gradients on quantum hardware
• Power of data in quantum machine learning
• A rigorous and robust quantum speed-up in supervised machine learning
• Training variational quantum algorithms is NP-hard
• Transfer learning in hybrid classical-quantum neural networks
• Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions
• Harnessing disordered quantum dynamics for machine learning
• Provably efficient machine learning for quantum many-body problems
• Beyond Barren Plateaus: Quantum Variational Algorithms Are Swamped With Traps
• Is quantum advantage the right goal for quantum machine learning?
• Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces
• Enhancing Generative Models via Quantum Correlations
• Does provable absence of barren plateaus imply classical simulability?
• Understanding quantum machine learning also requires rethinking generalization
• Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric
• Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data

Cited by in corpus (1)

• Learning complexity gradually in quantum machine learning models