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.
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.
Traditional machine learning (ML) and deep learning (DL) models have shown effectiveness in natural language processing (NLP) tasks, such as sentiment analysis.However, they often struggle with complex linguistic structures, such as sarcasm and implicit claims.This paper introduces a Quantum Long Short-Term Memory (QLSTM) framework for detecting sarcasm and identifying claims in text, aiming to enhance the analysis of complex sentences.We evaluate four approaches: (1) classical LSTM, (2) quantum framework using QLSTM, (3) voting ensemble combining classical and quantum LSTMs, and (4) hybrid framework integrating both types.The experimental results indicate that the QLSTM approach excels in sarcasm detection, while the voting framework performs best in claim identification.
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established. In this work, we propose a hybrid quantum-classical model of LSTM, which we dub QLSTM. We demonstrate that the proposed model successfully learns several kinds of temporal data. In particular, we show that for certain testing cases, this quantum version of LSTM converges faster, or equivalently, reaches a better accuracy, than its classical counterpart. Due to the variational nature of our approach, the requirements on qubit counts and circuit depth are eased, and our work thus paves the way toward implementing machine learning algorithms for sequence modeling such as natural language processing, speech recognition on noisy intermediate-scale quantum (NISQ) devices.
Abstract Hybrid quantum recurrent models are commonly evaluated against compact classical networks, making it diffcult to determine whether observed gains arise from the variational quantum circuit (VQC) or from the surrounding recurrent architecture. This study presents an architecture and parameter-controlled evaluation of quantum gated recurrent unit (QGRU) and quantum long short-term memory (QLSTM) models for one-step-ahead meteorological forecasting. Each quantum model is compared with both a compact, approximately parameter-matched recurrent baseline and an exact classical gate-core surrogate that preserves the hidden width, recurrent equations, gate-specific pre-/post-projections, optimization protocol, and total trainable-parameter count. All models use 17 input features, 24-step histories, a chronological 80/10/10 train–validation–test split, trainingonly standardization, and ten matched random seeds. QGRU achieves an RMSE of 1.1045 ± 0.0693, reducing test RMSE by 19.01% relative to the compact GRU and by 5.47% relative to its exact 773-parameter surrogate. It also attains a lower mean best validation MSE, reaches 95% of its achievable validation improvement after 5.5 ± 0.5 epochs, and exhibits a smaller final train–validation gap. QLSTM reduces test RMSE by 24.80% relative to the compact LSTM, while its exact 1,025-parameter surrogate achieves comparable predictive performance with slightly greater validation stability. Analysis of 1,949 epoch-level records across convergence, checkpoint quality, early stopping, generalization, and runtime demonstrates that VQC utility depends on the recurrent architecture in which it is embedded. All experiments are conducted using ideal noiseless statevector simulation.
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established. In this work, we propose a hybrid quantum-classical model of LSTM, which we dub QLSTM. We demonstrate that the proposed model successfully learns several kinds of temporal data. In particular, we show that for certain testing cases, this quantum version of LSTM converges faster, or equivalently, reaches a better accuracy, than its classical counterpart. Due to the variational nature of our approach, the requirements on qubit counts and circuit depth are eased, and our work thus paves the way toward implementing machine learning algorithms for sequence modeling on noisy intermediate-scale quantum (NISQ) devices.
Quantum Natural Language Processing (QNLP) represents a novel interdisciplinaryparadigm that integrates Quantum Computing with Natural Language Processing toenhance semantic modeling and computational efficiency. Despite the remarkable successof transformer-based architectures such as BERT and GPT, classical NLP systems requirelarge-scale computational resources and extensive parameter tuning. This paper proposesa hybrid quantum–classical architecture employing parameterized quantum circuits (PQCs)integrated with classical embedding layers for text classification tasks. The frameworkutilizes classical preprocessing, quantum state encoding, variational optimization, andclassical post-measurement classification. Experimental results on benchmark sentimentanalysis datasets demonstrate competitive accuracy with significantly reduced parametercomplexity. The findings indicate that hybrid QNLP models provide enhanced semanticcompositionality and efficient representation in Hilbert space while remaining feasiblewithin Noisy Intermediate-Scale Quantum (NISQ) constraints
Accurate state-of-health (SOH) estimation for lithium-ion batteries remains a challenging problem due to complex electrochemical degradation mechanisms and long-range temporal dependencies. In this work, we propose a quantum-enhanced recurrent framework, termed QLSTM, in which variational quantum circuits are directly embedded into the gating mechanisms of long short-term memory networks. By replacing classical affine transformations with parameterized unitary operations, the proposed model introduces structured nonlinear transformations into the recurrent state-transition process. Extensive experiments on multiple benchmark battery datasets demonstrate that QLSTM consistently outperforms classical sequence models in both predictive accuracy and robustness, achieving significant reductions in mean absolute error (MAE), with improvements on the order of 20% compared with classical LSTM baselines. Ablation studies further confirm that these improvements arise primarily from quantum-enhanced gating rather than input-level transformations. Additional analyses on qubit scaling and noise robustness reveal that model performance is governed by a balance between expressive capacity and trainability. These results provide empirical evidence that embedding quantum computational primitives within recurrent architectures offers a structurally grounded approach to improving sequence modeling capability. The proposed framework establishes a new design paradigm for integrating quantum operators into temporal learning models, with potential applications in complex dynamical system prediction tasks.