In this research we present a quantum circuit for estimating algorithmic information metrics like the universal prior distribution. 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. A classical exhaustive enumeration is shown for a few examples. The precise quantum circuit design that allows executing a superposition of automata is presented. As a use-case, an application framework for experimenting on DNA sequences for meta-biology is proposed. To our knowledge, this is the first time approximating algorithmic information is implemented for quantum computation. Our implementation on the OpenQL quantum programming language and the QX Simulator is copy-left and can be found on https://github.com/Advanced-Research-Centre/QuBio.

中文翻译：

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算法信息的量子加速估计

在这项研究中，我们提出了一个量子电路，用于估计通用先验分布等算法信息度量。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。显示了几个示例的经典详尽枚举。展示了允许执行自动机叠加的精确量子电路设计。作为一个用例，提出了一个用于元生物学 DNA 序列实验的应用框架。据我们所知，这是第一次为量子计算实现近似算法信息。