A Scalable Quantum Gate-Based Implementation for Causal Hypothesis Testing

In this work, a scalable quantum gate-based algorithm for accelerating causal inference is introduced. Specifically, the formalism of causal hypothesis testing presented in [Nat Commun 10, 1472 (2019)] is considered. Through the algorithm, the existing definition of error probability is generalized, which is a metric to distinguish between two competing causal hypotheses, to a practical scenario. The results on the Qiskit validate the predicted speedup and show that in the realistic scenario, the error probability depends on the distance between the competing hypotheses. To achieve this, the causal hypotheses are embedded as a circuit construction of the oracle. Furthermore, by assessing the complexity involved in implementing the algorithm's subcomponents, a numerical estimation of the resources required for the algorithm is offered. Finally, applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence are discussed. It expands the current framework by adjusting error probability based on process distance between the causal hypotheses. This implementation facilitates gate complexity estimation for the quantum algorithm. Stressing the significance of causal inference in machine learning and quantum networks, it anticipates applications in grasping general intelligence limits and bioinformatics. image