Practical distributed quantum information processing with LOCCNet
- URL: http://arxiv.org/abs/2101.12190v2
- Date: Wed, 22 Sep 2021 08:30:48 GMT
- Title: Practical distributed quantum information processing with LOCCNet
- Authors: Xuanqiang Zhao, Benchi Zhao, Zihe Wang, Zhixin Song, Xin Wang
- Abstract summary: We introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks.
As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation.
An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.
- Score: 8.633408580670812
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Distributed quantum information processing is essential for building quantum
networks and enabling more extensive quantum computations. In this regime,
several spatially separated parties share a multipartite quantum system, and
the most natural set of operations is Local Operations and Classical
Communication (LOCC). As a pivotal part in quantum information theory and
practice, LOCC has led to many vital protocols such as quantum teleportation.
However, designing practical LOCC protocols is challenging due to LOCC's
intractable structure and limitations set by near-term quantum devices. Here we
introduce LOCCNet, a machine learning framework facilitating protocol design
and optimization for distributed quantum information processing tasks. As
applications, we explore various quantum information tasks such as entanglement
distillation, quantum state discrimination, and quantum channel simulation. We
discover protocols with evident improvements, in particular, for entanglement
distillation with quantum states of interest in quantum information. Our
approach opens up new opportunities for exploring entanglement and its
applications with machine learning, which will potentially sharpen our
understanding of the power and limitations of LOCC. An implementation of
LOCCNet is available in Paddle Quantum, a quantum machine learning Python
package based on PaddlePaddle deep learning platform.
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