适配能力的密码分析与降空间量子验证
Capability-Adaptive Cryptanalysis with Reduced-Space Quantum Verification
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中文总结 AI 辅助
本研究提出适配能力的密码分析框架，整合三类密码分析方法并结合降空间量子验证，可将4096个密钥候选缩减至13个，使Grover验证迭代从50次减至2次，验证工作量降约25倍，为混合密码分析提供实用基础。
中文摘要 AI 辅助
将密码分析证据与量子验证高效整合是经典-量子混合密码分析领域的核心挑战。本研究提出一种适配能力的密码分析框架，该框架在通用候选空间缩减架构内统一了线性密码分析、差分密码分析及侧信道泄漏分析，随后通过振幅放大进行降空间量子验证。研究针对候选空间构建、自适应滤波、验证空间缩减及复杂度表征建立了形式化数学模型，相关理论结果明确了候选空间收缩与量子验证工作量之间的关系。进一步引入哈密顿量公式，为降空间验证过程提供了物理可实现的解释。基于统计生成的密码分析观测结果开展评估，结果表明，所提框架可将初始含4096个候选的密钥假设空间缩减至仅13个有效候选，整体缩减率约为99.683%。相应地，Grover算法的验证需求从50次迭代降至仅2次，验证工作量减少约25倍，同时降空间振幅放大达到约94.53%的目标态成功概率。这些结果表明，自适应密码分析滤波可在保持密码分析可接纳性的同时大幅降低量子验证复杂度，为适配能力的混合密码分析及降空间量子搜索提供了实用基础。
英文摘要
Efficient integration of cryptanalytic evidence with quantum verification remains a fundamental challenge in hybrid classical-quantum cryptanalysis. This work presents a capability-adaptive cryptanalytic framework that unifies linear cryptanalysis, differential cryptanalysis, and side-channel leakage analysis within a common candidate-space reduction architecture, followed by reduced-space quantum verification through amplitude amplification. A formal mathematical model is developed for candidate-space construction, adaptive filtering, verification-space reduction, and complexity characterization, supported by the oretical results establishing the relationship between candidate-space contraction and quantum verification effort. A Hamiltonian formulation is further introduced to provide a physically realizable interpretation of the reduced-space verification process. Evaluation using statistically generated cryptanalytic observations demonstrates that the proposed framework reduces an initial candidate-key hypothesis space of 4096 candidates to an effective candidate space of 13 hypotheses, corresponding to an overall reduction of approximately 99.683%. Consequently, the Grover verification requirement decreases from 50 iterations to only 2 iterations, yielding an approximately 25-fold reduction in verification effort, while reduced-space amplitude amplification achieves a target-state success probability of approximately 94.53%. These results demonstrate that adaptive cryptanalytic filtering can substantially reduce quantum verification complexity while preserving cryptanalytic admissibility, providing a practical foundation for capability-aware hybrid cryptanalysis and reduced-space quantum search.
