Quantum machine learning (QML) requires significant quantum resources to
achieve quantum advantage. 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. We
define the curriculum criteria based on the data density ratio between tasks to
determine the curriculum order. We also implement a dynamic learning schedule
to emphasize the significance of quantum data in optimizing the loss function.
Empirical evidence shows that Q-CurL enhances the training convergence and the
generalization for unitary learning tasks and improves the robustness of
quantum phase recognition tasks. Our framework provides a general learning
strategy, bringing QML closer to realizing practical advantages.

量子机器学习中，优化资源使用和设计量子架构是重要研究方向。我们提出了量子课程学习 (Q-CurL) 框架，通过引入简单任务和数据依据数据密度比例来确定学习顺序，优化损失函数，提高训练收敛性和泛化性，从而将量子机器学习更接近于实际应用价值。

量子课程学习

Quantum Curriculum Learning

Classical autoencoders are neural networks that can learn efficient low
dimensional representations of data in higher dimensional space. The task of an
autoencoder is, given an input $x$, is to map $x$ to a lower dimensional point
$y$ such that $x$ can likely be recovered from $y$. The structure of the
underlying autoencoder network can be chosen to represent the data on a smaller
dimension, effectively compressing the input. Inspired by this idea, we
introduce the model of a quantum autoencoder to perform similar tasks on
quantum data. The quantum autoencoder is trained to compress a particular
dataset of quantum states, where a classical compression algorithm cannot be
employed. The parameters of the quantum autoencoder are trained using classical
optimization algorithms. We show an example of a simple programmable circuit
that can be trained as an efficient autoencoder. We apply our model in the
context of quantum simulation to compress ground states of the Hubbard model
and molecular Hamiltonians.

本篇论文研究使用经典的优化算法来训练量子自编码器，从而实现对量子数据进行压缩和简化处理，且在 Hubbard 模型和分子哈密顿量的场景下应用自编码器进行量子模拟。