Fat-Tree QRAM: A High-Bandwidth Shared Quantum Random Access Memory for Parallel Queries

TL;DR

Fat-Tree QRAM is a new high-bandwidth quantum memory architecture that enables multiple parallel queries with efficient scaling, promising practical benefits for quantum computing applications.

Contribution

The paper introduces Fat-Tree QRAM, a novel architecture that allows pipelined parallel quantum queries with favorable scaling and proposes feasible superconducting circuit implementations.

Findings

Performs O(log N) queries in O(log N) time with O(N) qubits.

Supports pipelining of multiple quantum queries simultaneously.

Analyzes performance and fidelity of proposed implementations under realistic conditions.

Abstract

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.