Quantum Causal Unravelling
- URL: http://arxiv.org/abs/2109.13166v3
- Date: Thu, 23 Jun 2022 04:09:10 GMT
- Title: Quantum Causal Unravelling
- Authors: Ge Bai, Ya-Dong Wu, Yan Zhu, Masahito Hayashi, Giulio Chiribella
- Abstract summary: We develop the first efficient method for unravelling the causal structure of the interactions in a multipartite quantum process.
Our algorithms can be used to identify processes that can be characterized efficiently with the technique of quantum process tomography.
- Score: 44.356294905844834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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, under the assumption that the process has bounded information
loss and induces causal dependencies whose strength is above a fixed (but
otherwise arbitrary) threshold. Our method is based on a quantum algorithm
whose complexity scales polynomially in the total number of input/output
systems, in the dimension of the systems involved in each interaction, and in
the inverse of the chosen threshold for the strength of the causal
dependencies. 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
quantum networks, and to test the internal structure of uncharacterized quantum
circuits.
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