Quantum Curriculum Learning

TL;DR

Quantum curriculum learning (Q-CurL) introduces a structured training approach for quantum machine learning that improves convergence, robustness, and generalization, especially on noisy intermediate-scale quantum devices.

Contribution

This paper proposes the novel Q-CurL framework that employs a curriculum based on quantum data density ratios to enhance quantum learning efficiency and robustness.

Findings

Q-CurL improves training convergence in unitary learning.

Q-CurL enhances robustness in quantum phase recognition.

Q-CurL is effective in physics and quantum chemistry applications.

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.