Experimental Machine Learning of Quantum States

Abstract

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 Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

“…The quantum autoencoder belongs to the former category. Complementary to recent demonstrations of several classification tasks [21][22][23], Hamiltonian learning [24], and the reconstruction of quantum states [25], the present work experimentally establishes the compression of quantum data as another use of quantum machine learning.…”

“…As an additional benefit, the device is also robust to disturbances during its optimization routine, as discussed later. These advantages of optimizing directly based on experimental data are akin to previous observations in other systems [23,26].…”

Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

“…Depending on the problem at hand the speedup can be associated with various features of quantum physics. A number of proposals and experiments focused on QML have been reported, such works include for instance quantum support vector machines [ mann machines [3], quantum autoencoders [4], kernel methods [5], and quantum reinforcement learning [6,7]. In reinforcement learning a learning agent receives feedback in order to learn an optimal strategy for handling a nontrivial task.…”

Reconstruction of a Photonic Qubit State with Reinforcement Learning

“…Meanwhile, there are many recent works combining ML techniques with quantum information tools . These include expressing and witnessing quantum entanglement by artificial neural networks (ANN), analyzing and restructuring a quantum state by restricted Boltzmann machines (RBM), as well as detecting quantum change points, and learning Hamiltonians by Bayesian inference . Meanwhile, many quantum ML algorithms have already been applied in different experimental systems, such as photonics and nuclear magnetic resonance (NMR) systems.…”