quantum-safe: Bridging the Post-Quantum Production Gap with a Hybrid-by-Default Python Cryptography Library

TL;DR

This paper introduces 'quantum-safe', a Python cryptography library that addresses critical gaps in post-quantum cryptography deployment, including hybrid support, migration tools, and protocol integration, with comprehensive evaluation and performance analysis.

Contribution

The paper presents a complete Python library that closes post-quantum cryptography deployment gaps and provides a systematic evaluation of the ecosystem's readiness.

Findings

quantum-safe scores full on all eight production-readiness dimensions

X25519 + ML-KEM-768 handshake completes in 243 μs under Docker/Linux

ML-KEM-768 decapsulation has CoV = 3.9%, within AES-256-GCM noise floor

Abstract

The August 2024 finalisation of FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA) closed the algorithmic gap in post-quantum cryptography (PQC). The production gap -- hybrid combiners, versioned key formats, protocol helpers, and migration tooling -- remains open. We present quantum-safe, a Python library that closes all three critical gaps we identify, and a systematic evaluation of the nine-library ecosystem that quantifies them. We score nine PQC libraries across eight production-readiness dimensions. Three dimensions have coverage below 35%: hybrid KEM support (11%), migration tooling (22%), and protocol integration (33%). quantum-safe scores Full on all eight. The full API reduces the hybrid KEM task from 45 lines of manual combiner code to three lines, directly lowering the risk of insecure combiner implementations. We report the first statistically rigorous per-operation overhead measurement for a Python hybrid PQC library (3,000 iterations, CPU-pinned, bootstrapped 95% confidence intervals). A full X25519 + ML-KEM-768 handshake completes in 243 {\mu}s under Docker/Linux -- 0.5--2.5% of a typical TLS 1.3 round-trip budget. At 5,000 concurrent users, throughput holds at 2,848 ops/s with only 4.9% degradation versus the single-user baseline, confirming that liboqs releases the Python GIL during C-level operations. We introduce Coefficient of Variation (CoV) as a practical timing side-channel proxy across all FIPS 203/204 operations. ML-KEM-768 decapsulation achieves CoV = 3.9%, within the AES-256-GCM noise floor (2.1%). ML-DSA-65 signing shows CoV = 51.5%, expected from FIPS 204 rejection sampling, not a side-channel. This CoV methodology has not previously been applied to PQC library evaluation and provides a lightweight complement to formal constant-time verification tools. All results are reproducible via a single Docker command.