Quantum Random Access Memory (QRAM) is a crucial architectural component for querying classical or quantum data in superposition, enabling algorithms with wide-ranging applications in quantum arithmetic, quantum chemistry, machine learning, and quantum cryptography. In this work, we introduce Fat-Tree QRAM, a novel query architecture capable of pipelining multiple quantum queries simultaneously while maintaining desirable scalings in query speed and fidelity. Specifically, Fat-Tree QRAM performs $O(\log (N))$ independent queries in $O(\log (N))$ time using $O(N)$ qubits, offering immense parallelism benefits over traditional QRAM architectures. To demonstrate its experimental feasibility, we propose modular and on-chip implementations of Fat-Tree QRAM based on superconducting circuits and analyze their performance and fidelity under realistic parameters. Furthermore, a query scheduling protocol is presented to maximize hardware utilization and access the underlying data at an optimal rate. These results suggest that Fat-Tree QRAM is an attractive architecture in a shared memory system for practical quantum computing.

翻译：量子随机存取存储器（QRAM）是一种关键的架构组件，用于以叠加态查询经典或量子数据，从而在量子算术、量子化学、机器学习和量子密码学等广泛领域实现相关算法。本文提出胖树QRAM，这是一种新颖的查询架构，能够同时流水线处理多个量子查询，同时在查询速度和保真度方面保持理想的扩展性。具体而言，胖树QRAM使用$O(N)$个量子比特，在$O(\log (N))$时间内执行$O(\log (N))$个独立查询，相比传统QRAM架构提供了巨大的并行性优势。为论证其实验可行性，我们提出了基于超导电路的模块化和片上胖树QRAM实现方案，并在实际参数下分析了其性能与保真度。此外，本文提出了一种查询调度协议，以最大化硬件利用率并以最优速率访问底层数据。这些结果表明，胖树QRAM是实用量子计算中共享存储系统的一种极具吸引力的架构。