Quantum causal unravelling

Bai, Ge1,2; Wu, Ya-Dong1,2; Zhu, Yan1,2; Hayashi, Masahito3,4,5; Chiribella, Giulio1,2,6,7

2022-06-21

10.1038/s41534-022-00578-4

NPJ QUANTUM INFORMATION   Impact Factor & Quartile

2056-6387

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.

SCI ; EI

English

Others

National Natural Science Foundation of China[11675136] ; Hong Kong Research Grant Council[17300918,17307520,"SRFS2021-7S02"] ; John Templeton Foundation[61466] ; Guangdong Provincial Key Laboratory[2019B121203002]

Physics

Quantum Science & Technology ; Physics, Applied ; Physics, Atomic, Molecular & Chemical ; Physics, Condensed Matter

WOS:000814270300001

NATURE PORTFOLIO

20222612279442

Complex networks ; Quantum theory

Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Computer Systems and Equipment:722 ; Quantum Theory; Quantum Mechanics:931.4

2-s2.0-85132774165

Web of Science

1.Univ Hong Kong, Dept Comp Sci, QICI Quantum Informat & Computat Initiat, Pokfulam Rd, Hong Kong, Peoples R China
2.Univ Hong Kong, Dept Comp Sci, HKU Oxford Joint Lab Quantum Informat & Computat, Pokfulam Rd, Hong Kong, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Guangdong Prov Key Lab Quantum Sci & Engn, Shenzhen 518055, Peoples R China
5.Nagoya Univ, Grad Sch Math, Nagoya, Aichi 4648602, Japan
6.Univ Oxford, Dept Comp Sci, Parks Rd, Oxford OX1 3QD, England
7.Perimeter Inst Theoret Phys, 31 Caroline St North, Waterloo, ON N2L 2Y5, Canada

Bai, Ge,Wu, Ya-Dong,Zhu, Yan,et al. Quantum causal unravelling[J]. NPJ QUANTUM INFORMATION,2022,8(1).

Bai, Ge,Wu, Ya-Dong,Zhu, Yan,Hayashi, Masahito,&Chiribella, Giulio.(2022).Quantum causal unravelling.NPJ QUANTUM INFORMATION,8(1).

Bai, Ge,et al."Quantum causal unravelling".NPJ QUANTUM INFORMATION 8.1(2022).