Physically motivated quantum algorithms for specific near-term quantum
hardware will likely be the next frontier in quantum information science. Here,
we show how many of the features of neural networks for machine learning can
naturally be mapped into the quantum optical domain by introducing the quantum
optical neural network (QONN). Through numerical simulation and analysis we
train the QONN to perform a range of quantum information processing tasks,
including newly developed protocols for quantum optical state compression,
reinforcement learning, and black-box quantum simulation. We consistently
demonstrate our system can generalize from only a small set of training data
onto states for which it has not been trained. Our results indicate QONNs are a
powerful design tool for quantum optical systems and, leveraging advances in
integrated quantum photonics, a promising architecture for next generation
quantum processors.

提出了量子光学神经网络（QONN）的概念，并利用集成量子光子学等技术，通过数值模拟和分析培训 QONN，可执行一系列量子信息处理任务，包括压缩、强化学习、黑盒量子模拟等，并展示了其泛化数据的能力。结果表明 QONNs 是量子光学系统的有力设计工具，是下一代量子处理器的一种有前途的架构。

量子光学神经网络

Quantum optical neural networks

Integrated quantum photonics provides a scalable platform for the generation,
manipulation, and detection of optical quantum states by confining light inside
miniaturized waveguide circuits. Here we show the generation, manipulation, and
interferometric stage of homodyne detection of non-classical light on a single
device, a key step towards a fully integrated approach to quantum information
with continuous variables. We use a dynamically reconfigurable lithium niobate
waveguide network to generate and characterize squeezed vacuum and two-mode
entangled states, key resources for several quantum communication and computing
protocols. We measure a squeezing level of -1.38+-0.04 dB and demonstrate
entanglement by verifying an inseparability criterion I=0.77+-0.02<1. Our
platform can implement all the processes required for optical quantum
technology and its high nonlinearity and fast reconfigurability makes it ideal
for the realization of quantum computation with time encoded continuous
variable cluster states.

利用微型波导电路，我们展示了在单个器件上实现非经典光的生成、操纵和同震检测的过程，这对于连续变量量子信息的完全集成是一个关键步骤。我们提出了一个动态可重构的锂铌波导网络来生成和表征压缩真空和两模态纠缠态，用于量子通信和计算协议的关键资源。其高非线性和快速可重构性使我们的平台成为实现光学量子技术所需的所有过程的理想选择，并且非常适合实现基于时间编码的连续变量簇态的量子计算。

连续变量量子信息集成光子平台

Integrated photonic platform for quantum information with continuous  variables

The importance of integrated quantum photonics in the telecom band resides on
the possibility of interfacing with the optical network infrastructure
developed for classical communications. In this framework, femtosecond laser
written integrated photonic circuits, already assessed for quantum information
experiments in the 800 nm wavelength range, have great potentials. In fact
these circuits, written in glass, can be perfectly mode-matched at telecom
wavelength to the in/out coupling fibers, which is a key requirement for a
low-loss processing node in future quantum optical networks. In addition, for
several applications quantum photonic devices will also need to be dynamically
reconfigurable. Here we experimentally demonstrate the high performance of
femtosecond laser written photonic circuits for quantum experiments in the
telecom band and we show the use of thermal shifters, also fabricated by the
same femtosecond laser, to accurately tune them. State-of-the-art manipulation
of single and two-photon states is demonstrated, with fringe visibilities
greater than 95%. This opens the way to the realization of reconfigurable
quantum photonic circuits on this technological platform.

该研究论文表明，飞秒激光写入的光子电路在电信波段用于量子实验具有较高的性能，并展示了用热变换器对其进行精确调整的能力，这为在此技术平台上实现重构量子光学电路打开了道路。