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.
Highlights
In the past few decades, quantum technologies have been found to have an increasing number of powerful applications in areas including optimization[1,2], chemistry[3,4], security[5,6], and machine learning[7]
We introduce a machine learning framework for designing and optimizing Local Operations and Classical Communication (LOCC) protocols that are adaptive to near-term quantum devices, which consists of a set of parameterized quantum circuits (PQCs) representing local operations
We introduce LOCCNet, a machine learning framework that facilitates the design of LOCC protocols for various quantum information processing tasks, including entanglement distillation[24,25,26,27,28,29], quantum state discrimination[30,31,32,33,34,35,36,37,38,39,40,41], and quantum channel simulation[42,43,44,45,46,47,48]
Summary
In the past few decades, quantum technologies have been found to have an increasing number of powerful applications in areas including optimization[1,2], chemistry[3,4], security[5,6], and machine learning[7]. We introduce LOCCNet, a machine learning framework that facilitates the design of LOCC protocols for various quantum information processing tasks, including entanglement distillation[24,25,26,27,28,29], quantum state discrimination[30,31,32,33,34,35,36,37,38,39,40,41], and quantum channel simulation[42,43,44,45,46,47,48]. We apply LOCCNet to entanglement distillation and present selected results that reinforce the validity and the limited coherence time of local quantum memory To overcome these challenges, we propose to find LOCC protocols with the aid of machine learning, inspired by its recent success in various areas.
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