The first spatial 2D quantum walk on a photonic chip with thousands of nodes is realized for future analog quantum computing.

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in “big data.” A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.

The low-decoherence regime plays a key role in constructing multi-particle quantum systems and has therefore been constantly pursued in order to build quantum simulators and quantum computers in a scalable fashion. Quantum error correction and quantum topological computing have been proved to be able to protect quantumness but have not yet been experimentally realized. Recently, topological boundary states are found to be inherently stable and are capable of protecting physical fields from dissipation and disorder, which inspires the application of such topological protection on quantum correlation. Here we present an experimental demonstration of topological protection of two-photon quantum states against the decoherence in diffusion on a photonic chip. By analyzing the quantum correlation of photons out from the topologically nontrivial boundary state, we obtain a high cross-correlation and a strong violation of Cauchy–Schwarz inequality up to 30 standard deviations. We further prepare different quantum sources and experimentally confirm that the topological protection is robust to the wavelength difference as well as distinguishability of two photons. Our results, together with our integrated implementation, provide an alternative way of protecting quantumness and may inspire many more explorations in “quantum topological photonics”, a crossover between topological photonics and quantum information.

Twisted light carrying orbital angular momentum (OAM) provides an additional degree of freedom for modern optics and an emerging resource for both classical and quantum information technologies. Its inherently infinite dimensions can potentially be exploited by using mode multiplexing to enhance data capacity for sustaining the unprecedented growth in big data and internet traffic and can be encoded to build large-scale quantum computing machines in high-dimensional Hilbert space. While the emission of twisted light from the surface of integrated devices to free space has been widely investigated, the transmission and processing inside a photonic chip remain to be addressed. Here, we present the first laser-direct-written waveguide being capable of supporting OAM modes and experimentally demonstrate a faithful mapping of twisted light into and out of a photonic chip. The states ${\mathrm{OAM}}_0$, ${\mathrm{OAM}}_{-1}$, ${\mathrm{OAM}}_{+1}$, and their superpositions can transmit through the photonic chip with a total efficiency up to 60% with minimal crosstalk. In addition, we present the transmission of quantum twisted light states of single photons and measure the output states with single-photon imaging. Our results may add OAM as a new degree of freedom to be transmitted and manipulated in a photonic chip for high-capacity communication and high-dimensional quantum information processing.

A hybrid quantum network integrates room-temperature atomic and all-optical memories for scalable quantum information processing.