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.

分布式量子信息处理对于构建量子网络和实现更广泛的量子计算至关重要。在这种制度下，几个空间分离的各方共享一个多方量子系统，最自然的一组操作是本地操作和经典通信（LOCC）。作为量子信息理论和实践的关键部分，LOCC 导致了许多重要的协议，例如量子隐形传态。然而，由于 LOCC 难以处理的结构和近期量子设备设置的限制，设计实用的 LOCC 协议具有挑战性。在这里，我们介绍了 LOCCNet，这是一种机器学习框架，可促进分布式量子信息处理任务的协议设计和优化。作为应用，我们探索各种量子信息任务，例如纠缠蒸馏、量子状态判别和量子信道模拟。我们发现了具有明显改进的协议，特别是对于量子信息中感兴趣的量子态的纠缠蒸馏。我们的方法为探索纠缠及其在机器学习中的应用开辟了新的机会，这可能会加深我们对 LOCC 的力量和局限性的理解。LOCCNet 的实现在 Paddle Quantum 中可用，这是一个基于 PaddlePaddle 深度学习平台的量子机器学习 Python 包。我们的方法为探索纠缠及其在机器学习中的应用开辟了新的机会，这可能会加深我们对 LOCC 的力量和局限性的理解。LOCCNet 的实现在 Paddle Quantum 中可用，这是一个基于 PaddlePaddle 深度学习平台的量子机器学习 Python 包。我们的方法为探索纠缠及其在机器学习中的应用开辟了新的机会，这可能会加深我们对 LOCC 的力量和局限性的理解。LOCCNet 的实现在 Paddle Quantum 中可用，这是一个基于 PaddlePaddle 深度学习平台的量子机器学习 Python 包。