用量子机器学习让量子模拟更高效，首次实现二维系统编译。
Scalable quantum dynamics compilation via quantum machine learning
- 利用量子机器学习在少量态上训练，泛化到高纠缠态
- 一维系统性能超越现有方法，二维条带也实现资源优势
- 适合近期内存有限的量子处理器做高效模拟
量子动力学编译是提升量子模拟效率的关键任务：目标是将多量子比特目标演化合成尽可能少的基门电路。与如 Trotter 化等确定性方法相比，变分量子编译（VQC）通过变分优化降低门成本同时保持高精度。本文探索一种 VQC 方案，利用量子机器学习中的分布外泛化结果：仅在少量纯态数据集上学习给定多体动力学的作用，即可获得能泛化至高度纠缠态（如哈随机态）的酉电路。训练效率高，可借助张量网络方法压缩时间演化后的纯态，利用其低纠缠特性。该方法在一维系统中超越当前最优编译结果，在系统规模和精度上均表现更优。首次将 VQC 扩展至二维条带系统，采用准一维处理方式，显著优于标准 Trotter 化方法，凸显其在近中期量子处理器上推进量子模拟任务的潜力。
原文摘要 · Abstract (English)
Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such as Trotterization, variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy. In this work, we explore the potential of a VQC scheme by making use of out-of-distribution generalization results in quantum machine learning (QML): By learning the action of a given many-body dynamics on a small data set of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states. The efficiency in training allows us to use tensor network methods to compress such time-evolved product states by exploiting their low entanglement features. Our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension ($1$D). For the first time, we extend VQC to systems on two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resource advantage over standard Trotterization methods, highlighting the method's promise for advancing quantum simulation tasks on near-term quantum processors.
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