Quantum state preparation, as a general process of loading classical data to quantum device, is essential for end-to-end implementation of quantum algorithms. Yet, existing methods suffer from either high circuit depth or complicated hardware, limiting their practicality and robustness. In this work, we overcome these limitations with a bucket-brigade approach. The tree architectures of our hardware represents the simplest connectivity required for achieving sub-exponential circuit depth. Leveraging the bucket-brigade mechanism that can suppress the error propagation between different branches, our approach exhibit exponential improvement on the robustness compared to existing depth-optimal methods. More specifically, the infidelity scales as $O(\text{polylog}(N))$ with data size $N$, as oppose to $O(N)$ for conventional methods. Moreover, our approach is the first to simultaneously achieve linear Clifford$+T$ circuit depth, gate count number, and space-time allocation. These advancements offer the opportunity for processing big data in both near-term and fault-tolerant quantum devices.

翻译：量子态制备作为将经典数据加载到量子设备的一般过程，对于量子算法的端到端实现至关重要。然而，现有方法要么需要较高的电路深度，要么依赖于复杂的硬件结构，这限制了其实用性和鲁棒性。在本工作中，我们通过一种"桶旅"（bucket-brigade）方法克服了这些限制。我们硬件的树状架构代表了实现亚指数电路深度所需的最简连接性。利用能够抑制不同分支间误差传播的桶旅机制，我们的方法在鲁棒性上相比现有深度最优方法展现出指数级改进。具体而言，保真度损失随数据规模$N$按$O(\text{polylog}(N))$缩放，而传统方法为$O(N)$。此外，我们的方法首次同时实现了线性的Clifford$+T$电路深度、门数量以及时空资源分配。这些进展为在近期和容错量子设备中处理大数据提供了新的机遇。