ACSS-q: Algorithmic complexity for short strings via quantum accelerated approach

TL;DR

This paper introduces a quantum circuit for estimating algorithmic complexity of short strings, enabling faster inference of data structures and causal models, with applications in biological data analysis.

Contribution

It presents a novel quantum circuit design based on superposition of automata for approximating algorithmic complexity, advancing quantum methods in data analysis.

Findings

Quantum circuit accelerates complexity estimation

Potential to improve protein interaction analysis

Bridges causal gap in entropy-based methods

Abstract

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.