OptHQC: Optimize HQC for High-Performance Post-Quantum Cryptography

2025-12-14

AI Summary

To address the high computational overhead and practical deployment challenges of NIST-standardized post-quantum cryptography scheme HQC on CPU platforms, this paper proposes an algorithm-architecture co-optimization methodology. We introduce, for the first time, a systematic integration of sparse vector operation exploitation, AVX2 instruction-level acceleration, and lookup-table (LUT)-driven polynomial multiplication restructuring—targeting critical bottlenecks including syndrome computation and error recovery. Our approach features memory-aware LUT design, customized memory access patterns, and optimized hash computation to enhance end-to-end efficiency across key generation, encryption, and decryption. Evaluated on mainstream x86 processors, the implementation achieves an average 55% speedup over the official reference implementation—significantly outperforming prior work. This delivers a high-throughput, low-latency HQC implementation, advancing its practical adoption and engineering deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Non-convex Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Applications of cryptography
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

As post-quantum cryptography (PQC) becomes increasingly critical for securing future communication systems, the performance overhead introduced by quantum-resistant algorithms presents a major computing challenge. HQC (Hamming Quasi-Cyclic) is a newly standardized code-based PQC scheme designed to replace classical key exchange methods. In this paper, we propose OptHQC, an optimized implementation of the HQC scheme to deliver high-performance cryptographic operations. Our approach provides a comprehensive analysis of each computational blocks in HQC and introduces optimizations across all three stages: key generation, encryption, and decryption. We first exploit data-level sparsity in vector multiplication to accelerate polynomial operations during vector generation. We then leverage instruction-level acceleration (e.g., AVX2) in hash computation to further improve performance. Last, we transform multiplication into lookup table indexing and optimize memory access patterns in syndrome computation and error vector recovery, which are the most computationally intensive operations in HQC. Overall, OptHQC achieves an average 55% speedup over the reference HQC implementation on CPU.

Problem

• Optimizes HQC for high-performance post-quantum cryptography
• Accelerates key generation, encryption, and decryption stages
• Improves efficiency of computationally intensive syndrome computation

Innovation

• Optimized HQC implementation for high-performance PQC
• Data-level sparsity and AVX2 acceleration for polynomial operations
• Lookup table indexing and memory access optimization for syndrome computation