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至量子设备的通用过程，对于量子算法的端到端实现至关重要。然而，现有方法要么需要高电路深度，要么依赖复杂的硬件结构，限制了其实用性与鲁棒性。本研究通过采用桶链式架构克服了这些限制。我们的硬件树状结构代表了实现亚指数级电路深度所需的最简连接性。借助能够抑制不同分支间误差传播的桶链式机制，本方法在鲁棒性上相比现有深度最优方法实现了指数级提升。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，本方法首次同时实现了线性 Clifford$+T$ 电路深度、门数量级与时空资源分配。这些进展为在近期及容错量子设备中处理大规模数据提供了可能。