TY - JOUR
T1 - Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications
AU - Chittoor, Hari Hara Suthan
AU - Simeone, Osvaldo
N1 - Funding Information:
This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No. 725732), by the European Union’s Horizon Europe project CENTRIC (101096379), and by an Open Fellowship of the EPSRC (EP/W024101/1).
Publisher Copyright:
© 2023 by the authors.
PY - 2023/12/14
Y1 - 2023/12/14
N2 - Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which classical communication takes place over noisy channels, and we propose to address the design of LOCC protocols in this setting via the use of quantum machine learning tools. We specifically focus on the important tasks of quantum entanglement distillation and quantum state discrimination, and implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity and average success probability in the respective tasks, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.
AB - Distributed quantum information processing protocols such as quantum entanglement distillation and quantum state discrimination rely on local operations and classical communications (LOCC). Existing LOCC-based protocols typically assume the availability of ideal, noiseless, communication channels. In this paper, we study the case in which classical communication takes place over noisy channels, and we propose to address the design of LOCC protocols in this setting via the use of quantum machine learning tools. We specifically focus on the important tasks of quantum entanglement distillation and quantum state discrimination, and implement local processing through parameterized quantum circuits (PQCs) that are optimized to maximize the average fidelity and average success probability in the respective tasks, while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), is shown to have significant advantages over existing protocols designed for noiseless communications.
UR - http://www.scopus.com/inward/record.url?scp=85148945770&partnerID=8YFLogxK
U2 - 10.3390/e25020352
DO - 10.3390/e25020352
M3 - Article
SN - 1099-4300
VL - 25
JO - Entropy
JF - Entropy
IS - 2
M1 - 352
ER -