A Quantum Approximate Optimization Algorithm-based Decoder Architecture for NextG Wireless Channel Codes
Srikar Kasi, James Sud, Kyle Jamieson, Gokul Subramanian Ravi

TL;DR
This paper introduces FDeQ, a quantum approximate optimization algorithm-based decoder architecture for NextG wireless channel codes, demonstrating potential advantages in decoding performance and resource efficiency over classical methods.
Contribution
The paper proposes a novel QAOA-based FEC decoder architecture, FDeQ, optimized for NextG wireless codes, integrating temporal similarity to improve decoding efficiency and feasibility.
Findings
FDeQ achieves error performance comparable to classical decoders at low code lengths.
Resource estimation indicates future quantum devices could outperform classical decoders in certain scenarios.
FDeQ exploits fixed code structures to accelerate quantum decoding processes.
Abstract
Forward Error Correction (FEC) provides reliable data flow in wireless networks despite the presence of noise and interference. However, its processing demands significant fraction of a wireless network's resources, due to its computationally-expensive decoding process. This forces network designers to compromise between performance and implementation complexity. In this paper, we investigate a novel processing architecture for FEC decoding, one based on the quantum approximate optimization algorithm (QAOA), to evaluate the potential of this emerging quantum compute approach in resolving the decoding performance-complexity tradeoff. We present FDeQ, a QAOA-based FEC Decoder design targeting the popular NextG wireless Low Density Parity Check (LDPC) and Polar codes. To accelerate QAOA-based decoding towards practical utility, FDeQ exploits temporal similarity among the FEC decoding…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Advanced Wireless Communication Techniques · Error Correcting Code Techniques
