Quantum Causal Unravelling

Abstract

Complex processes often arise from sequences of simpler interactions involving a few particles at a time. These interactions, however, may not be directly accessible to experiments. Here we develop the first efficient method for unravelling the causal structure of the interactions in a multipartite quantum process, using only access to its input and output systems. Our method is based on a quantum algorithm whose complexity scales polynomially for all processes with bounded information loss. Under additional assumptions, we also provide a second algorithm that has lower complexity and requires only local state preparation and local measurements. Our algorithms can be used to identify processes that can be characterized efficiently with the technique of quantum process tomography. Similarly, they can be used to identify useful communication channels in a quantum networks, and to test the structure of uncharacterized quantum circuits.

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

“…Quantum information enables a richer spectrum of causal relations that is not possible to access via classical statistics. Most research in this direction is towards exploring causality in the quantum context [16][17][18][19][20][21]. Our focus in this work is specifically using the quantum formulation to provide a computational advantage with respect to a classical technique on classical data.…”