Generating Haar-Uniform Randomness Using Stochastic Quantum Walks on a Photonic Chip.

As random operations for quantum systems are intensively used in various quantum information tasks, a trustworthy measure of the randomness in quantum operations is highly demanded. The Haar measure of randomness is a useful tool with wide applications, such as boson sampling. Recently, a theoretical protocol was proposed to combine quantum control theory and driven stochastic quantum walks to generate Haar-uniform random operations. This opens up a promising route to converting classical randomness to quantum randomness. Here, we implement a two-dimensional stochastic quantum walk on the integrated photonic chip and demonstrate that the average of all distribution profiles converges to the even distribution when the evolution length increases, suggesting the 1-pad Haar-uniform randomness. We further show that our two-dimensional array outperforms the one-dimensional array of the same number of waveguide for the speed of convergence. Our Letter demonstrates a scalable and robust way to generate Haar-uniform randomness that can provide useful building blocks to boost future quantum information techniques.

Thresholded single-photon underwater imaging and detection.

Optical underwater target imaging and detection have been a tough but significant challenge in deep-sea exploration. Distant reflected signals drown in various underwater noises due to strong absorption and scattering, resulting in degraded image contrast and reduced detection range. Single-photon feature operating at the fundamental limit of the classical electromagnetic waves can broaden the realm of quantum technologies. Here we experimentally demonstrate a thresholded single-photon imaging and detection scheme to extract photon signals from the noisy underwater environment. We reconstruct the images obtained in a high-loss underwater environment by using photon-limited computational algorithms. Furthermore, we achieve a capability of underwater detection down to 0.8 photons per pulse at Jerlov type III water up to 50 meters, which is equivalent to more than 9 attenuation lengths. The results break the limits of classical underwater imaging and detection and may lead to many quantum-enhanced applications, like air-to-sea target tracking and deep-sea optical exploration.

Experimental Machine Learning of Quantum States.

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.

Experimental Test of Tracking the King Problem.

In quantum theory, the retrodiction problem is not as clear as its classical counterpart because of the uncertainty principle of quantum mechanics. In classical physics, the measurement outcomes of the present state can be used directly for predicting the future events and inferring the past events which is known as retrodiction. However, as a probabilistic theory, quantum-mechanical retrodiction is a nontrivial problem that has been investigated for a long time, of which the Mean King Problem is one of the most extensively studied issues. Here, we present the first experimental test of a variant of the Mean King Problem, which has a more stringent regulation and is termed "Tracking the King." We demonstrate that Alice, by harnessing the shared entanglement and controlled-not gate, can successfully retrodict the choice of King's measurement without knowing any measurement outcome. Our results also provide a counterintuitive quantum communication to deliver information hidden in the choice of measurement.