Quantum Accelerated Causal Tomography: Circuit Considerations For Applications In Bioinformatics and AGI

Abstract

In this research we study quantum computing algorithms for accelerating causal inference. Specifically, we investigate the formulation of causal hypothesis testing presented in [Nat Commun 10, 1472(2019]. The theoretical description is constructed as a scalable quantum gate-based algorithm on qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Our experiments on a simulator platform validates the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence.

Visualizing Quantum Circuit Probability: Estimating Quantum State Complexity for Quantum Program Synthesis

“…Another crucial aspect of intelligence [71] is the understanding of cause-effect relations. Quantum acceleration of causal inference [72][73][74] can benefit from the knowledge of the probability distribution of causal oracles, a subset of quantum processes that embed specific properties of the problem. Besides causal inference, similar techniques can be applied to other statistical relational learning applications like probabilistic logic networks [75] and quantum variational algorithms.…”