arXiv:2205.13376

Finding efficient observable operators in entanglement detection via convolutional neural network

Zi-Qi Lian, You-Yang Zhou, Liujun Wang, Qing Chen

Published May 26, 2022• 0 citations

Abstract

In quantum information, it is of high importance to efficiently detect entanglement. Generally, it needs quantum tomography to obtain state density matrix. However, it would consumes a lot of measurement resources, and the key is how to reduce the consumption. In this paper, we discovered the relationship between convolutional layer of artificial neural network and the average value of an observable operator in quantum mechanics. Then we devise a branching convolutional neural network which can be applied to detect entanglement in 2-qubit quantum system. Here, we detect the entanglement of Werner state, generalized Werner state and general 2-qubit states, and observable operators which are appropriate for detection can be automatically found. Beside, compared with privious works, our method can achieve higher accuracy with fewer measurements for quantum states with specific form. The results show that the convolutional neural network is very useful for efficiently detecting quantum entanglement.

DOI: 10.48550/arxiv.2205.13376

Related papers

High-Accuracy Entanglement Detection via a Convolutional Neural Network with Noise Resistance

Qian Sun, Yanyan Song, Zhichuan Liao +1 more

Quantum entanglement detection is one of the fundamental tasks in quantum information science. Conventional methods for quantum state tomography exhibit limitations in scalability as the number of qubits increases, leading to exponential growth in the number of unknown parameters and required measurements. Consequently, the accuracy enhancement achieved by these methods is constrained. In response to this challenge, we developed a tailored convolutional neural network (CNN) model capable of effectively detecting entanglement in two-qubit quantum states, achieving an accuracy exceeding 97.5%. Notably, even in the presence of noise, this model retains its robust performance, displaying resilience against a tolerable level of noise contamination. Furthermore, the inherent generalization power of CNNs allows our model, which was initially trained on a specific spectrum of quantum states, to extend its applicability to wider states, positioning it as an outstanding tool for the further application of machine learning in the field of quantum computing, opening up new pathways for solving entanglement detection problems in quantum information.

Applied SciencesOct 16, 20242 citations

Low-cost detection of high-dimensional multipartite entanglement structures

Rui Li, Shikun Zhang, Zheng Qin +3 more

Quantum entanglement detection and characterization are crucial for various quantum information processes. Most existing methods for entanglement detection rely heavily on a complete description of the quantum state, which requires numerous measurements and complex setups. This makes these theoretically sound approaches costly and impractical, as the system size increases. In this work, we propose a multiview neural-network model to generate representations suitable for entanglement structure detection. The number of required quantum measurements shows a significant reduction compared to conventional quantum state tomography approaches as the qubit number increases. This remarkable reduction in resource costs makes it possible to detect specific entanglement structures in large-scale systems. Numerical simulations show that our method achieves over 95% detection accuracy for up to 19 qubit systems. By enabling a universal, flexible, and resource-efficient analysis of entanglement structures, our approach enhances the capability of utilizing quantum states across a wide range of applications.

arXiv:2408.13015Physical Review AppliedApr 15, 20255 citations

Enhanced Quantum Entanglement Detection of General Two Qubits Systems Based on Modified CNN‐BiLSTM Model

Qian Sun, Zhichuan Liao, Nan Jiang

Abstract Entanglement is a key element in quantum information processing. The detection of entanglement is crucial in many long‐range quantum information tasks, including secure communication and fundamental tests of quantum physics, but it is also highly resource‐intensive. Even for simple 2‐qubits systems, satisfactory detection is challenging. In this work, a modified entanglement detection model combining a convolutional neural network (CNN) and a bidirectional long short‐term memory network (BiLSTM) is proposed. It shows that the proposed model can effectively extract the deep features and correlations, enabling accurate classification of simple quantum states, even with only a few tens of training samples. When trained with a large number of highly random samples, the model exhibits outstanding fitting capability, resulting in the reliable classification of nearly all common 2‐qubits systems. Furthermore, the model exhibits exceptional adaptability and significant application potential in higher‐dimensional systems.

Advanced Quantum TechnologiesOct 25, 20240 citations

Finding semi-optimal measurements for entanglement detection using autoencoder neural networks

Mohammad Yosefpor, Mohammad Reza Mostaan, Sadegh Raeisi

Abstract Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena. This problem is however both computationally and experimentally challenging. Here we use autoencoder neural networks to find semi-optimal set of incomplete measurements that are most informative for the detection of entangled states . We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly. This result paves the way for automatic development of efficient entanglement witnesses and entanglement detection using machine learning techniques.

arXiv:2003.07031Quantum Science and TechnologyJul 6, 202014 citations

Detecting quantum entanglement with unsupervised learning

Yiwei Chen, Yu Pan, Guofeng Zhang +1 more

Abstract Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional and multipartite quantum systems. In this work, we exploit the convexity of samples without the desired quantum features and design an unsupervised machine learning method to detect the presence of such features as anomalies. Particularly, in the context of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from two-qubit to ten-qubit systems, that our network is able to achieve high detection accuracy which is above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, and thus our work could provide a powerful tool to extract quantum features hidden in multipartite quantum data.

arXiv:2103.04804Quantum Science and TechnologyOct 19, 202161 citations

Detection of quantum steering for the generalized Werner states via artificial neural networks

Guo‐Zhu Pan, Shuting Zou, Ming Yang +2 more

Abstract Quantum steering is an important nonlocal resource and has a wide range of applications in quantum information processing. Although a lot of steering criteria have been proposed, it is still very difficult to efficiently detect quantum steering in experiment. Here we employ machine learning techniques to tackle the problem of quantum steering detection in two-qubit system. The quantum steering and un-steering inequalities are combined together, so as to construct quantum steering classifiers for the generalized Werner states via artificial neural networks. More steerable and unsteerable quantum states can be identified by the classifiers proposed here than by the quantum steering inequality or un-steering inequality, which provides a new way to detect steering with only partial information of the given quantum states. We consider two types of artificial neural networks, one is the single-layer perceptron and the other is the multi-layer perceptron. The result shows that the multi-layer perceptron outperforms the single-layer perceptron in terms of accuracy. Compared with the existing quantum steering criteria, our methods do not require the whole information of the quantum state, and the steering of it is detected by using state-independent measurements, so it is easy to realize in experiment.

Laser Physics LettersDec 27, 20230 citations