Abstract Detecting rhyme in Persian poetry is computationally difficult because the language's written form often diverges from its phonological realization. While massive deep learning models can address this ambiguity, they require excessive computational resources. To solve this efficiency bottleneck, we propose CRNN-VQC, a hybrid architecture that integrates Convolutional-Recurrent Neural Networks (CNN–BiGRU) with Variational Quantum Circuits (VQCs). Instead of standard dense layers, our approach embeds character-level features into an 8-qubit parameterized quantum circuit via a multi-stage data re-uploading scheme, enabling expressive non-linear mapping in a high-dimensional quantum space. We evaluated the model on a hand-curated corpus of 20,171 hemistich pairs, achieving a test accuracy of 96.95% and an F1-score of 96.30%, which statistically outperforms equivalent classical baselines (p < 0.001). Crucially, CRNN-VQC matches the performance of large pre-trained language models like ParsBERT while using approximately three orders of magnitude fewer parameters (163k versus 162M). Furthermore, robustness tests under simulated quantum noise confirm the model’s stability, indicating its feasibility for deployment on Noisy Intermediate-Scale Quantum (NISQ) devices. These findings demonstrate that hybrid quantum–classical frameworks offer a highly parameter-efficient alternative for analyzing the phonology of morphologically rich languages.
Abstract Detecting rhyme in Persian poetry is computationally difficult because the language's written form often diverges from its phonological realization. While massive deep learning models can address this ambiguity, they require excessive computational resources. To solve this efficiency bottleneck, we propose CRNN-VQC, a hybrid architecture that integrates Convolutional-Recurrent Neural Networks (CNN–BiGRU) with Variational Quantum Circuits (VQCs). Instead of standard dense layers, our approach embeds character-level features into an 8-qubit parameterized quantum circuit via a multi-stage data re-uploading scheme, enabling expressive non-linear mapping in a high-dimensional quantum space. We evaluated the model on a hand-curated corpus of 20,171 hemistich pairs, achieving a test accuracy of 96.95% and an F1-score of 96.30%, which statistically outperforms equivalent classical baselines (p < 0.001). Crucially, CRNN-VQC matches the performance of large pre-trained language models like ParsBERT while using approximately three orders of magnitude fewer parameters (163k versus 162M). Furthermore, robustness tests under simulated quantum noise confirm the model’s stability, indicating its feasibility for deployment on Noisy Intermediate-Scale Quantum (NISQ) devices. These findings demonstrate that hybrid quantum–classical frameworks offer a highly parameter-efficient alternative for analyzing the phonology of morphologically rich languages.
Abstract Sarcasm detection is a challenging task in Natural Language Processing (NLP) due to its reliance on subtle and implicit linguistic cues that are often not explicitly expressed in text. Designing models capable of capturing such nuanced patterns remains an open problem. In this work, we investigate hybrid quantum–classical recurrent architectures for sarcasm detection by conducting a comparative study of classical and quantum-enhanced sequence models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their quantum counterparts, Quantum LSTM (QLSTM) and Quantum GRU (QGRU).We employ a pre-trained BERT model to extract contextualized token embeddings, which are subsequently compressed through classical neural layers to ensure compatibility with Noisy Intermediate-Scale Quantum (NISQ) devices. In the proposed quantum-enhanced models, classical linear transformations within recurrent gates are replaced by Variational Quantum Circuits (VQCs), enabling the exploitation of quantum properties such as superposition and entanglement within a hybrid learning framework.Experimental results on a balanced sarcasm-labeled dataset indicate that both QLSTM and QGRU achieve performance levels comparable to their classical counterparts, despite utilizing significantly fewer trainable parameters. Notably, the quantum-enhanced models require approximately 10% of the parameters used by their classical equivalents while maintaining similar macro F1 scores. These findings suggest that hybrid quantum recurrent architectures can serve as compact and parameter-efficient alternatives to classical sequence models, highlighting their potential applicability in quantum natural language processing under near-term hardware constraints.
The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks. Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility. Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges. Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.
The objective of this research is to develop a Hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance.The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity.Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%.Statistical analysis confirmed the significance of these improvements.Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model's robustness.The results highlight the hybrid QC-CNN's potential for efficient and scalable quantum-enhanced deep learning applications.Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.
This study proposes a hybrid quantum-classical framework for depression detection from social media text, integrating a frozen DistilBERT encoder with a variational quantum circuit (VQC)-based classification layer. The motivation stems from challenges in clinical NLP, including overfitting on limited datasets and high parameter overhead in conventional deep learning classifiers. Experiments are conducted on a balanced subset of the Reddit Self-Reported Depression Diagnosis (RSDD) dataset comprising 6,000 users. The proposed model is evaluated against classical baselines, including TF-IDF with logistic regression and a fine-tuned DistilBERT model. Results indicate that the hybrid approach achieves competitive performance, with an F1-score of 0.925 (±0.009) and improved recall (0.942 ± 0.015) compared to the classical DistilBERT baseline. Additionally, the quantum classification layer requires significantly fewer trainable parameters (72) compared to the classical dense head, demonstrating improved parameter efficiency at the classification stage. While the results suggest that variational quantum circuits can serve as an alternative non-linear classifier in low-data settings, the findings are based on simulation and require further validation on real quantum hardware. This work contributes to the emerging area of quantum natural language processing by providing an empirical evaluation of hybrid architectures on a real-world clinical text dataset.
Given the recent prominence of Noisy Intermediate Scale Quantum (NISQ) devices, hybrid quantum-classical neural networks have recently drawn a lot of excitement as a potential solution to improving learning efficiency. Our current paper describes a Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) which is defined as a pretrained backbone of ResNet-18 and a small quantum-enhanced feature-transformation layer of Qiskit. The convolutional high-dimensional features are reduced to the classical eight-dimensional one, which in turn is encoded into a four-qubit parameterized quantum circuit. The resultant quantum expectation values are combined with the compressed features to come up with a hybridized representation of twelve dimensions, used to classify multi-class dataset. Experimental analysis of CIFAR10 data has shown that the suggested HQCNN achieves a test accuracy of 94.74% and improves class-wise precision, recall, and F1-scores than a number of existing hybrid models while using a significantly smaller number of latent features. More investigation into the quantum module indicates that it is predictable about scalability to batch, resistant to perturbations in features, and efficiently performed by evaluating circuit vectors. These results indicate that compact and expressive representations of images that can be helpful in the NISQ era can be acquired using hybrid architectures, thus providing a practical way to forward the benefits of quantum to modern deep-learning pipelines.
Rigorous empirical validation of quantum machine learning on natural language tasks remains scarce. We evaluate a 10-qubit hybrid quantum-classical variational circuit (2,148 parameters) for paraphrase detection across three benchmarks: MRPC, Quora Question Pairs (QQP), and adversarial PAWS. On QQP (n = 10 seeds), the circuit achieves 75.53% +/- 0.75%. accuracy, statistically outperforming parameter-matched classical baselines (DeepMLP: p = 0.015, Cohen's d = 1.20; F1: p < 0.001, d = 2.32) and surpassing DistilBERT-4bit with 31,191 fewer parameters. On MRPC the optimal 2-layer variant reaches 92% of BERT-base accuracy at 54,000 lower parameter cost. Circuit depth analysis reveals a dataset-depth scaling effect; entanglement analysis via the Meyer-Wallach measure identifies multi-qubit entanglement as the primary performance driver (r = 0.85 across four variants). Adversarial evaluation on PAWS reveals emergent robustness: 98.2% recall versus 81.6% classical (+16.6 pp, d = 1.24, p < 0.001), without adversarial training. These results constitute the first systematic parameter-matched multi-benchmark empirical validation of hybrid variational circuits for NLP. All results are from classical simulation; hardware validation is future work.