TY - GEN
T1 - Quantum Network Tomography with Multi-party State Distribution
AU - De Andrade, Matheus Guedes
AU - Diaz, Jaime
AU - Navas, Jake
AU - Guha, Saikat
AU - Montano, Ines
AU - Smith, Brian
AU - Raymer, Michael
AU - Towsley, Don
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The fragile nature of quantum information makes it practically impossible to completely isolate a quantum state from noise under quantum channel transmissions. Quantum networks are complex systems formed by the interconnection of quantum processing devices through quantum channels. In this context, characterizing how channels introduce noise in transmitted quantum states is of paramount importance. Precise descriptions of the error distributions introduced by non-unitary quantum channels can inform quantum error correction protocols to tailor operations for the particular error model. In addition, characterizing such errors by monitoring the network with end-to-end measurements enables end-nodes to infer the status of network links. In this work, we address the end-to-end characterization of quantum channels in a quantum network by introducing the problem of Quantum Network Tomography. The solution for this problem is an estimator for parameters that define a Kraus decomposition for all quantum channels in the network, using measurements performed exclusively in the end-nodes. We study this problem in detail for the case of arbitrary star quantum networks with quantum channels described by a single Pauli operator, like bit-flip quantum channels. We provide solutions for such networks with polynomial sample complexity. Our solutions provide evidence that pre-shared entanglement brings advantages for estimation in terms of the identifiability of parameters.
AB - The fragile nature of quantum information makes it practically impossible to completely isolate a quantum state from noise under quantum channel transmissions. Quantum networks are complex systems formed by the interconnection of quantum processing devices through quantum channels. In this context, characterizing how channels introduce noise in transmitted quantum states is of paramount importance. Precise descriptions of the error distributions introduced by non-unitary quantum channels can inform quantum error correction protocols to tailor operations for the particular error model. In addition, characterizing such errors by monitoring the network with end-to-end measurements enables end-nodes to infer the status of network links. In this work, we address the end-to-end characterization of quantum channels in a quantum network by introducing the problem of Quantum Network Tomography. The solution for this problem is an estimator for parameters that define a Kraus decomposition for all quantum channels in the network, using measurements performed exclusively in the end-nodes. We study this problem in detail for the case of arbitrary star quantum networks with quantum channels described by a single Pauli operator, like bit-flip quantum channels. We provide solutions for such networks with polynomial sample complexity. Our solutions provide evidence that pre-shared entanglement brings advantages for estimation in terms of the identifiability of parameters.
KW - Network Tomography
KW - Quantum Estimation
KW - Quantum Network Tomography
KW - Quantum Networks
KW - Quantum State Distribution
KW - Quantum Tomography
UR - http://www.scopus.com/inward/record.url?scp=85143610793&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143610793&partnerID=8YFLogxK
U2 - 10.1109/QCE53715.2022.00061
DO - 10.1109/QCE53715.2022.00061
M3 - Conference contribution
AN - SCOPUS:85143610793
T3 - Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022
SP - 400
EP - 409
BT - Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Quantum Computing and Engineering, QCE 2022
Y2 - 18 September 2022 through 23 September 2022
ER -