对比神经网络与量子电路的训练效率，发现量子电路用更少参数达到相近性能。
Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
- 在简单任务上比较不同参数量的神经网络与量子电路
- 量子电路性能接近神经网络，但参数量减少显著
- 适合关注量子机器学习未来潜力的研究者
近年来，神经网络（NNs）推动了机器学习的重大进展。然而，随着任务复杂度提升，神经网络通常需要大量可训练参数，导致计算和能耗增加。变分量子电路（VQCs）提供了一种有前景的替代方案：利用量子力学捕捉复杂关系，且通常所需参数更少。本文在简单的监督学习和强化学习任务上评估了神经网络与变分量子电路，考察了不同参数规模的模型。我们对量子电路进行模拟，并在真实量子硬件上执行部分训练过程以估算实际训练时间。结果表明，尽管训练耗时更长，变分量子电路可在性能上匹配神经网络，同时使用显著更少的参数。随着量子技术、算法及架构的持续进步，我们认为变分量子电路有望在某些机器学习任务中占据优势。
原文摘要 · Abstract (English)
In recent years, neural networks (NNs) have driven significant advances in machine learning. However, as tasks grow more complex, NNs often require large numbers of trainable parameters, which increases computational and energy demands. Variational quantum circuits (VQCs) offer a promising alternative: they leverage quantum mechanics to capture intricate relationships and typically need fewer parameters. In this work, we evaluate NNs and VQCs on simple supervised and reinforcement learning tasks, examining models with different parameter sizes. We simulate VQCs and execute selected parts of the training process on real quantum hardware to approximate actual training times. Our results show that VQCs can match NNs in performance while using significantly fewer parameters, despite longer training durations. As quantum technology and algorithms advance, and VQC architectures improve, we posit that VQCs could become advantageous for certain machine learning tasks.
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