Giovannetti, Lloyd, and Maccone (2008) proposed a quantum random access memory (QRAM) architecture to retrieve arbitrary superpositions of N (quantum) memory cells via quantum switches and \(O(\log (N))\) address qubits. Toward physical QRAM implementations, Chen et al. (2021) recently showed that QRAM maps natively onto optically connected quantum networks with \(O(\log (N))\) overhead and built-in error detection. However, modeling QRAM on large networks has been stymied by exponentially rising classical compute requirements. Here, we address this bottleneck by: (1) introducing a resource-efficient method for simulating large-scale noisy entanglement, allowing us to evaluate hundreds and even thousands of qubits under various noise channels; and (2) analyzing Chen et al.’s network-based QRAM as an application at the scale of quantum data centers or near-term quantum internet; and (3) introducing a modified network-based QRAM architecture to improve quantum fidelity and access rate. We conclude that network-based QRAM could be built with existing or near-term technologies leveraging photonic integrated circuits and atomic or atom-like quantum memories.

Giovannetti、Lloyd 和 Maccone (2008) 提出了一种量子随机存取存储器 (QRAM) 架构，可通过量子开关和\(O(\log (N))\)地址量子位检索N （量子）存储单元的任意叠加。对于物理 QRAM 实现，Chen 等人。(2021) 最近表明，QRAM 可以原生映射到光学连接的量子网络，具有O(log (N)))开销和内置错误检测。然而，在大型网络上进行 QRAM 建模一直受到指数级增长的经典计算需求的阻碍。在这里，我们通过以下方式解决这个瓶颈：（1）引入一种资源有效的方法来模拟大规模噪声纠缠，使我们能够在各种噪声通道下评估数百甚至数千个量子位；(2) 分析 Chen 等人的基于网络的 QRAM 作为量子数据中心或近期量子互联网规模的应用；(3)引入改进的基于网络的QRAM架构以提高量子保真度和访问速率。我们的结论是，基于网络的 QRAM 可以利用现有或近期的技术，利用光子集成电路和原子或类原子量子存储器来构建。