Optical neural network quantum state tomography
TLDR
In this paper , an optical neural network (ONN) for photonic polarization qubit quantum state tomography (QST) has been proposed, which can determine the phase parameter of the qubit state accurately.Abstract:
Abstract. Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the “imaging” technique in quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. We build and demonstrate an optical neural network (ONN) for photonic polarization qubit QST. The ONN is equipped with built-in optical nonlinear activation functions based on electromagnetically induced transparency. The experimental results show that our ONN can determine the phase parameter of the qubit state accurately. As optics are highly desired for quantum interconnections, our ONN-QST may contribute to the realization of optical quantum networks and inspire the ideas combining artificial optical intelligence with quantum information studies.read more
Citations
More filters
Journal ArticleDOI
Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
TL;DR: In this paper , an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement was proposed, where optical layers extract invisible topological charge information from incoming light and a shallow electronic layer predicts the exact spectrum.
Journal ArticleDOI
Imperfect Quantum Photonic Neural Networks
TL;DR: In this article , the authors investigated the limitations of non-ideal quantum photonic neural networks that suffer from fabrication imperfections leading to unbalanced photon loss and imperfect routing, and weak nonlinearities, showing that they can learn to overcome most of these errors.
Journal ArticleDOI
Software-defined nanophotonic devices and systems empowered by machine learning
TL;DR: In this article , the authors introduce the concept of software-defined nanophotonics and summarize the interdisciplinary research that bridges the research in nanophotonic and intelligence algorithms, especially machine learning algorithms, in the device design, measurement and system setup.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
The quantum internet
TL;DR: In this paper, the authors proposed a method for quantum interconnects, which convert quantum states from one physical system to those of another in a reversible manner, allowing the distribution of entanglement across the network and teleportation of quantum states between nodes.
Journal ArticleDOI
Electromagnetically induced transparency : Optics in coherent media
TL;DR: In this paper, the authors consider the atomic dynamics and the optical response of the medium to a continuous-wave laser and show how coherently prepared media can be used to improve frequency conversion in nonlinear optical mixing experiments.
Posted Content
Empirical Evaluation of Rectified Activations in Convolutional Network.
TL;DR: The experiments suggest that incorporating a non-zero slope for negative part in rectified activation units could consistently improve the results, and are negative on the common belief that sparsity is the key of good performance in ReLU.
Journal ArticleDOI
Deep learning with coherent nanophotonic circuits
TL;DR: A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.