  Giovannetti, Lloyd, and Maccone [Phys. Rev. Lett. 100, 160501] proposed a
quantum random access memory (QRAM) architecture to retrieve arbitrary
superpositions of $N$ (quantum) memory cells via $O(\log(N))$ quantum switches
and $O(\log(N))$ address qubits. Towards physical QRAM implementations, Chen et
al. [PRX Quantum 2, 030319] 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: (i) 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 (ii) analyzing Chen et
al.'s network-based QRAM as an application at the scale of quantum data centers
or near-term quantum internet; and (iii) 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.
