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Lecture notes in computer science

Detecting Entanglement in Quantum Systems with Artificial Neural Network

Joanna Wiśniewska, Marek Sawerwain

Published Jan 1, 2015• 3 citations
Publisher
DOI
10.1007/978-3-319-15702-3_35

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We design and successfully implement artificial neural networks (ANNs) to detect and classify entanglement for three-qubit systems using limited state features. The overall design principle is a feed forward neural network (FFNN), with the output layer consisting of a single neuron for the detection of genuine multipartite entanglement (GME) and six neurons for the classification problem corresponding to six entanglement classes under stochastic local operations and classical communication (SLOCC). The models are trained and validated on a simulated dataset of randomly generated states. We achieve high accuracy, around 98%, for detecting GME as well as for SLOCC classification. Remarkably, we find that feeding only 7 diagonal elements of the density matrix into the ANN results in an accuracy greater than 94% for both the tasks, showcasing the strength of the method in reducing the required input data while maintaining efficient performance. Reducing the feature set makes it easier to apply ANN models for entanglement classification, particularly in resource-constrained environments, without sacrificing accuracy. The performance of the ANN models was further evaluated by introducing white noise into the data set, and the results indicate that the models are robust and are able to well tolerate noise.
The detection of fundamental quantum resources-namely coherence, discord, and entanglement-benchmarks the metrological power of quantum sensing networks. Traditional methods for certifying these resources, like exhaustive optimization-based tomographic procedures, are resource-intensive and vary significantly. This paper proposes a framework for identifying metrologically useful quantum sensing probes by detecting fundamental quantum resources. Herein, we introduce a witness-based certification method that is experimentally accessible and efficient, though it has limitations in reliability and universality. To address these, we employ artificial neural networks (ANNs) to classify quantum states into resourceful and resourceless states, enhancing the scope and reliability of proposed framework. The performance of ANN-based quantum state classification is also analyzed, positioning ANNs as effective tools for data-driven detection of metrologically resourceful states in quantum sensing tasks.
A quantum neural networks(QNN) approach for mutiuser detection in CDMA system is theoretically proposed.Quantum neural networks is the combination of quantum computing and artificial neural network(ANN).Taking advantages of the properties of quantum mechanics such as quantum parallelism and entanglement.The computer simulation demonstrates that this algorithm has great advantages of better tolerance to coding error and the far-near effect.These performances of the approach in flat Rayleigh fading environment are also satisfactory.