A scalable quantum gate-based implementation for causal hypothesis testing

TL;DR

This paper develops a scalable quantum gate-based algorithm for causal hypothesis testing, demonstrating potential speedups in causal inference tasks relevant to bioinformatics and AI.

Contribution

It introduces a practical quantum circuit implementation for causal hypothesis testing, addressing previous error probability issues and validating speedup via simulations.

Findings

Quantum circuit implementation for causal hypothesis testing

Validated speedup on simulator platform

Discussed applications in bioinformatics and AI

Abstract

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [\textit{Nat Commun} 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence.