标题： 相空间框架用于噪声中等规模量子光学神经网络 显示英文标题

标题： Phase-Space Framework for Noisy Intermediate-Scale Quantum Optical Neural Networks

摘要： 量子光学神经网络（QONNs）通过利用经典光学和量子光学的优势，实现了超越经典极限的信息处理。 然而，由于描述量子网络所需的希尔伯特空间呈指数增长，大规模玻色子晶格的模拟仍然是一个重大挑战。 因此，以往的理论研究仅限于小规模系统，使得多模式QONNs的行为在很大程度上尚未被探索。 本工作提出了一种基于相空间正P方法的高效计算框架，用于模拟玻色子类脑系统。 这种方法提供了一种进入此前无法触及区域的视角，使在各种量子机器学习任务中验证大规模玻色子网络成为可能，例如量子状态分类和量子状态特征预测。 我们的结果表明，大型量子储备的性能并不随着玻色子模式数量的增加而单调改善，而是由非线性、储备大小和输入模式的平均占据数之间的相互作用所驱动的复杂依赖关系。 这些发现对于设计和优化未来量子类脑计算设备的光学玻色子储备至关重要。 显示英文摘要

摘要： Quantum optical neural networks (QONNs) enable information processing beyond classical limits by exploiting the advantages of classical and quantum optics. However, simulation of large-scale bosonic lattices remains a significant challenge due to the exponential growth of the Hilbert space required to describe a quantum network accurately. Consequently, previous theoretical studies have been limited to small-scale systems, leaving the behaviour of multimode QONNs largely unexplored. This work presents an efficient computational framework based on the phase-space positive-P method for simulating bosonic neuromorphic systems. This approach provides a view to previously inaccessible regimes, allowing the validation of large-scale bosonic networks in various quantum machine learning tasks such as quantum state classification and quantum state feature prediction. Our results show that the performance of a large quantum reservoir does not improve monotonously with the number of bosonic modes, instead following a complex dependence driven by the interplay of nonlinearity, reservoir size, and the average occupation of the input mode. These findings are essential for designing and optimising optical bosonic reservoirs for future quantum neuromorphic computing devices.