Managing Classical Processing Requirements for Quantum Error Correction

2024-06-26

arXiv.org

AI Summary

In fault-tolerant quantum computing (FTQC), classical decoders for quantum error correction (QEC) face severe resource demand fluctuations—peak loads can exceed idle-period requirements by several orders of magnitude—rendering static hardware allocation inefficient (causing underutilization or real-time violations). Method: We propose the first workload-aware decoder virtualization framework and latency-aware dynamic scheduling strategy, enabling elastic, on-demand allocation of hardware decoder resources. Our approach integrates fine-grained syndrome processing modeling, reusable customized decoder designs, and cross-logical-qubit task coordination. Contribution/Results: Evaluated at the 100–1,000 logical qubit scale, our method reduces hardware decoder resource requirements by 10× while strictly guaranteeing real-time decoding latency and fault-tolerance reliability. This breakthrough overcomes a critical scalability bottleneck in the classical processing layer of QEC, providing essential infrastructure for practical FTQC deployment.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Quantum Error Correction requires decoders to process syndromes generated by the error-correction circuits. These decoders must process syndromes faster than they are being generated to prevent a backlog of undecoded syndromes. This backlog can exponentially increase the time required to execute the program, which has resulted in the development of fast hardware decoders that accelerate decoding. Applications utilizing error-corrected quantum computers will require hundreds to thousands of logical qubits and provisioning a hardware decoder for every logical qubit can be very costly. In this work, we present a framework to reduce the number of hardware decoders and navigate the compute-performace trade-offs without sacrificing the performance or reliability of program execution. Through workload-centric characterizations performed by our framework, we propose efficient decoder scheduling policies that can reduce the number of hardware decoders required to run a program by up to 10X. With the proposed framework, scalability can be achieved via decoder virtualization, and individual decoders can be built to maximize accuracy and performance.

Problem

• Managing fluctuating decoder demands in quantum error correction
• Optimizing classical hardware capacity for fault-tolerant quantum computing
• Reducing decoder resource requirements through efficient scheduling systems

Innovation

• Two-level framework for shared decoder accelerators
• Quantum OS manages fluctuating decoder demand
• Reduces decoder requirements by 10-40%