AQER：一种可扩展且高效的量子计算机数据加载器
AQER: a scalable and efficient data loader for digital quantum computers
AI总结：
AQER是一种基于信息论的高效量子数据加载方法，通过系统减少目标状态纠缠，提升量子计算的数据处理能力。
摘要：
数字量子计算有望提供超越经典系统的计算能力，但其能力常受到量子资源稀缺的挑战。在此背景下，如何高效地将经典或量子数据加载到量子电路中是一个关键瓶颈。近似量子加载器（AQLs）通过在保真度和电路复杂性之间取得平衡，为解决这一问题提供了可行的方案。然而，大多数现有的AQL方法要么是启发式的，要么只对特定类型的输入提供保证，缺乏一个通用的理论框架。为了解决这一差距，我们重新表述了大多数AQL方法，将其纳入一个统一的框架，并建立了关于其近似误差的信息论界限。我们的分析揭示，当加载电路应用于目标状态时，所准备的状态与目标状态之间的可实现不保真度与子系统间的总纠缠熵成线性关系。基于此，我们开发了AQER，一种可扩展的AQL方法，通过系统地减少目标状态中的纠缠来构建加载电路。我们进行了系统性的实验来评估AQER的有效性，使用合成数据集、经典图像和语言数据集以及具有最多50个量子比特的量子多体态数据集。结果表明，AQER在准确性和门效率方面都优于现有方法。我们的工作为可扩展的量子数据处理和实际量子计算应用铺平了道路。
英文摘要：
Digital quantum computing promises to offer computational capabilities beyond the reach of classical systems, yet its capabilities are often challenged by scarce quantum resources. A critical bottleneck in this context is how to load classical or quantum data into quantum circuits efficiently. Approximate quantum loaders (AQLs) provide a viable solution to this problem by balancing fidelity and circuit complexity. However, most existing AQL methods are either heuristic or provide guarantees only for specific input types, and a general theoretical framework is still lacking. To address this gap, here we reformulate most AQL methods into a unified framework and establish information-theoretic bounds on their approximation error. Our analysis reveals that the achievable infidelity between the prepared state and target state scales linearly with the total entanglement entropy across subsystems when the loading circuit is applied to the target state. In light of this, we develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states. We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits. The results show that AQER consistently outperforms existing methods in both accuracy and gate efficiency. Our work paves the way for scalable quantum data processing and real-world quantum computing applications.
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