Back to News
quantum-computing

QRAM: A Survey and Critique

Samuel Jaques and Arthur G. Rattew
Loading...
2 min read
0 likes
⚡ Quantum Brief
A 2025 study categorizes quantum random-access memory (QRAM) into two models: active (requiring external control per query) and passive (self-sustaining post-initiation), revealing critical trade-offs in quantum computing architectures. Active QRAM’s quantum advantage diminishes in applications like quantum linear algebra, as its control hardware could instead run parallel classical algorithms with comparable speed, undermining asymptotic performance gains. Passive QRAM avoids active model constraints but relies on physically dubious "ballistic computation" assumptions, with current proposals failing to meet scalability or cost-efficiency requirements. The analysis concludes that cheap, scalable passive QRAM remains implausible under existing designs, citing fundamental obstacles tied to quantum memory demands—though these challenges aren’t mathematically proven insurmountable. Circuit-based QRAM retains utility for specific applications, with the study providing updated techniques for algorithm designers leveraging its limited but practical benefits.
AI Audio Summary
0:00 / 0:00
Click to play
99d6e805-9ff2-439c-ba4f-1232238f23dd.jpeg
Quantum News · Media Library

Quantum 9, 1922 (2025).https://doi.org/10.22331/q-2025-12-02-1922Quantum random-access memory (QRAM) is a mechanism to access data (quantum or classical) based on addresses which are themselves a quantum state. QRAM has a long and controversial history, and here we survey and expand arguments and constructions for and against. We use two primary categories of QRAM from the literature: (1) active, which requires external intervention and control for each QRAM query (e.g. the error-corrected circuit model), and (2) passive, which requires no external input or energy once the query is initiated. In the active model, there is a powerful opportunity cost argument: in many applications, one could repurpose the control hardware for the qubits in the QRAM (or the qubits themselves) to run an extremely parallel classical algorithm to achieve the same results just as fast. We apply these arguments in detail to quantum linear algebra and prove that most asymptotic quantum advantage disappears with active QRAM systems, with some nuance related to the architectural assumptions. Escaping the constraints of active QRAM requires ballistic computation with passive memory, which creates an array of dubious physical assumptions, which we examine in detail. Considering these details, in everything we could find, all non-circuit QRAM proposals fall short in one aspect or another. In summary, we conclude that cheap, asymptotically scalable passive QRAM is unlikely with existing proposals, due to fundamental obstacles that we highlight. These obstacles are deeply rooted in the requirements of QRAM, but are not provably inevitable; we hope that our results will help guide research into QRAM technologies that circumvent or mitigate these obstacles. Finally, circuit-based QRAM still helps in many applications, and so we additionally provide a survey of state-of-the-art techniques as a resource for algorithm designers using QRAM.

Read Original

Tags

energy-climate
quantum-advantage
quantum-algorithms
quantum-hardware

Source Information

Source: Quantum Journal

Discussion

0 professional contributions

Sign in to join this professional discussion.

Be the first to add a constructive contribution.