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

2025-02-10

AI Summary

To address the low throughput and poor scalability of conventional serial-query quantum random access memory (QRAM), this paper proposes a high-bandwidth shared QRAM architecture. Methodologically, it introduces: (1) a novel fat-tree topology enabling parallel execution of O(log N) independent quantum queries under superposition; (2) a multi-level quantum routing scheme, pipelined address decoding, parallel data access protocol, and a dedicated query scheduler to maximize hardware utilization; and (3) modular on-chip integration using superconducting circuits. Theoretically, the architecture achieves O(log N) query latency and O(N) resource overhead. Noise simulations under realistic device parameters confirm high fidelity. This work is the first to jointly optimize throughput and fidelity in shared quantum memory, establishing a scalable hardware foundation for practical quantum computing systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• Enhance quantum query parallelism
• Improve QRAM speed and fidelity
• Enable scalable quantum memory architecture

Innovation

• Fat-Tree QRAM enables high-bandwidth parallel queries.
• Uses superconducting circuits for modular on-chip implementation.
• Implements query scheduling for optimal hardware utilization.