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.
翻译:分布式量子信息处理对于建立量子网络和促成更广泛的量子计算至关重要。在这个制度中,几个空间分离的当事方共享一个多部分量子系统,而最自然的操作组是本地操作和古典通信(LOCC)系统。作为量子信息理论和实践的关键部分,LOCC导致了许多重要协议,例如量子传送。然而,设计实用的LOCC协议由于LOC的棘手结构和近期量子装置设置的限制而具有挑战性。在这里我们引入了LOCNet,这是一个机器学习框架,便利了分布式量子信息处理任务的协议设计和优化。作为应用,我们探索了各种量子信息任务,如缠绕式蒸馏、量子状态区分和量子频道模拟。我们发现了协议,但有了明显的改进,特别是将量子信息中的量子蒸馏与量子状态混合。我们的方法为探索纠结及其与机器学习的应用开辟了新的机会,这有可能加深我们对LCCCC的能量和局限性的理解。LCCNet的实施在Paddleaddleast Quantultum, 一种量子机器学习 Pydledledle Padledledal平台上可以使用。