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arXiv 2202.05412cs.LO
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量子连续时间马尔可夫链的连续随机逻辑验证

Checking Continuous Stochastic Logic against Quantum Continuous-Time Markov Chains

Ming Xu, Jingyi Mei, Ji Guan, Yuxin Deng, Nengkun Yu

AI总结:

本文针对量子连续时间马尔可夫链,提出结合代数与数值方法的连续随机逻辑模型检验方案,确立了CSL的可判定性并保证计算效率,还给出示例演示方法应用。

摘要:

近几十年来,量子系统验证已引发广泛研究关注。本文研究量子连续时间马尔可夫链(quantum CTMC)的定量模型检验问题。量子CTMC的分支时间性质由连续随机逻辑(CSL)刻画,该逻辑广泛用于经典CTMC等实时系统的验证。CSL公式检验的核心是处理多阶段until公式。本文提出一种代数方法,通过合适的投影、矩阵指数运算与定积分运算,对路径公式的概率测度进行符号计算,由此确立了CSL的可判定性。为提升效率,本文结合数值方法,保证算法时间复杂度关于输入模型的编码规模为多项式级,关于输入公式的规模为线性级。本文进一步给出阿波罗尼斯网络的运行示例,以演示所提方法的应用。

英文摘要:

Verifying quantum systems has attracted a lot of interest in the last decades.In this paper, we study the quantitative model-checking of quantum continuous-time Markov chains (quantum CTMCs). The branching-time properties of quantum CTMCs are specified by continuous stochastic logic (CSL), which is well-known for verifying real-time systems, including classical CTMCs. The core of checking the CSL formulas lies in tackling multiphase until formulas. We develop an algebraic method using proper projection, matrix exponentiation, and definite integration to symbolically calculate the probability measures of path formulas. Thus the decidability of CSL is established. To be efficient, numerical methods are incorporated to guarantee that the time complexity is polynomial in the encoding size of the input model and linear in the size of the input formula. A running example of Apollonian networks is further provided to demonstrate our method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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