发表机构
The University of Western Australia; University Paris City and University of Reunion（西澳大学; 巴黎城市大学和留尼汪大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Q-MERGE通过分段并行制备与SELECT-SWAP组合，将大规模振幅编码态制备的不保真度提升七个数量级，并减少辅助量子比特，可扩展至海量经典数据编码。
AI 中文摘要
经典数据的量子处理从根本上依赖于将经典数据集高效映射到量子态的振幅上。然而，制备大规模振幅编码态仍然是量子计算中的一个主要瓶颈。在本文中，我们提出了一种名为Q-MERGE的新型态制备框架，该框架通过将大型目标态划分为M个n量子比特段，独立并行地制备这些段，然后使用SELECT-SWAP操作和测量将它们相干地组合成单个振幅编码态，从而解决了这一瓶颈。Q-MERGE对段级制备方法不可知，允许将现有技术应用于较小的子问题，同时提供电路深度与量子比特数量之间可调的权衡。通过中间电路测量和制备寄存器重用，所需的辅助量子比特可以从O(Mn)减少到O(n)。应用于真实的128×256超声数据集，Q-MERGE实现了1.066×10^-8的不保真度，而使用相同底层方法直接制备的不保真度为3.413×10^-1，提升了七个数量级。我们在Quantinuum System Model H2俘获离子量子计算机上实验证明了其可行性，并使用阴影重叠层析成像验证了制备态。对Haar随机态的数值分析表明，在最多M=10^7个段的情况下，成功概率保持一致，支持Q-MERGE在编码大规模经典数据集方面的可扩展性。
英文摘要
Quantum processing of classical data fundamentally relies on efficiently mapping classical datasets onto the amplitudes of quantum states. Preparing large amplitude-encoded states, however, remains a major bottleneck in quantum computing. In this paper, we introduce a novel state-preparation framework we call Q-MERGE, that addresses this bottleneck by partitioning a large target state into $M$ $n$-qubit segments, preparing these segments independently and in parallel, and then coherently combining them into a single amplitude-encoded state using SELECT-SWAP operations and measurement. Q-MERGE is agnostic to the segment-level preparation method, allowing existing techniques to be applied to smaller subproblems while providing a tunable trade-off between circuit depth and qubit count. With mid-circuit measurement and preparation-register reuse, the required ancilla qubits can be reduced from $\mathcal{O}(Mn)$ to $\mathcal{O}(n)$. Applied to a real-world \(128\times256\) ultrasound dataset, Q-MERGE achieves an infidelity of $1.066\times10^{-8}$, compared with $3.413\times10^{-1}$ for direct preparation using the same underlying method, a seven-order of magnitude improvement. We demonstrate its feasibility experimentally on the Quantinuum System Model H2 trapped-ion quantum computer and validate the prepared state using shadow-overlap tomography. Numerical analysis of Haar-random states indicates consistent success probability for up to $M=10^7$ segments, supporting the scalability of Q-MERGE for encoding massive classical datasets.
