深度学习在大规模量子系统中未必必要，传统机器学习已足够好。
Rethink the Role of Deep Learning towards Large-scale Quantum Systems
- 在相同量子资源下对比深度与传统机器学习模型性能。
- 127个量子比特的实验显示，传统模型表现不逊于深度学习。
- 输入特征随机化不影响深度学习预测，说明其依赖性不足。
表征量子系统的基态性质是理解其行为的基础，但计算上极具挑战。近年来，人工智能引入了新方法，各类机器学习（ML）和深度学习（DL）模型被用于此任务。然而，现有研究常使用不同或不切实际的量子资源构建数据集，导致比较不公平。为此，我们在三类哈密顿量上系统性地对深度学习模型与传统机器学习方法进行基准测试，规模扩展至127个量子比特，并在三个关键基态学习任务中保持同等量子资源消耗。结果表明，传统机器学习模型在所有任务中的表现均与深度学习相当甚至更优。此外，随机化测试显示，测量输入特征对深度学习模型预测性能影响极小。这些发现质疑了当前深度学习模型在许多量子系统学习场景中的必要性，并为其实效利用提供了重要启示。
原文摘要 · Abstract (English)
Characterizing the ground state properties of quantum systems is fundamental to capturing their behavior but computationally challenging. Recent advances in AI have introduced novel approaches, with diverse machine learning (ML) and deep learning (DL) models proposed for this purpose. However, the necessity and specific role of DL models in these tasks remain unclear, as prior studies often employ varied or impractical quantum resources to construct datasets, resulting in unfair comparisons. To address this, we systematically benchmark DL models against traditional ML approaches across three families of Hamiltonian, scaling up to 127 qubits in three crucial ground-state learning tasks while enforcing equivalent quantum resource usage. Our results reveal that ML models often achieve performance comparable to or even exceeding that of DL approaches across all tasks. Furthermore, a randomization test demonstrates that measurement input features have minimal impact on DL models' prediction performance. These findings challenge the necessity of current DL models in many quantum system learning scenarios and provide valuable insights into their effective utilization.
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