In this research we present a quantum circuit for estimating algorithmic complexity using the coding theorem method. This accelerates inferring algorithmic structure in data for discovering causal generative models. The computation model is restricted in time and space resources to make it computable in approximating the target metrics. The quantum circuit design based on our earlier work that allows executing a superposition of automata is presented. As a use-case, an application framework for protein-protein interaction ontology based on algorithmic complexity is proposed. Using small-scale quantum computers, this has the potential to enhance the results of classical block decomposition method towards bridging the causal gap in entropy based methods.

中文翻译：

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ACSS-q：通过量子加速方法的短字符串算法复杂度

在这项研究中，我们提出了一种使用编码定理方法估计算法复杂性的量子电路。这加速了推断数据中的算法结构以发现因果生成模型。计算模型在时间和空间资源上受到限制，以使其在逼近目标度量时可计算。提出了基于我们早期工作的量子电路设计，该设计允许执行自动机的叠加。作为一个用例，提出了一个基于算法复杂性的蛋白质-蛋白质相互作用本体的应用框架。使用小型量子计算机，这有可能增强经典块分解方法的结果，以弥合基于熵的方法中的因果差距。