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Applied Sciences

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

Qian Sun, Yanyan Song, Zhichuan Liao, Nan Jiang

Published Oct 16, 2024• 2 citations
Publisher

Abstract

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.

DOI
10.3390/app14209418

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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.