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IEEE Access

Generalized Ansatz for High-Accuracy Quantum Sentiment Classification Using Variational Quantum Algorithm Scheme

Muhammad Rifat Abiwardani, Fariska Zakhralativa Ruskanda, Infall Syafalni, Rahmat Mulyawan, Akio Higo

Published Jan 1, 2025• 1 citation
Publisher

Abstract

This paper investigates sentiment classification using QNLP within the Variational Quantum Algorithms (VQA) framework, which offers a practical approach for current hardware limitations by optimizing parameterized quantum circuits through classical computation. Designing an effective VQA ansatz is a key challenge because it directly influences the model’s expressiveness and trainability. To address this, a new ansatz called the GeneralQC ansatz is proposed by combining the preliminary SimpleSA ansatz with the circuit-centric quantum classifier, aiming to enhance performance by integrating the strengths of both methods. Three instantiations of the GeneralQC ansatz—H-CRx, Rx-CNOT, and Rx-CRz—are explored under the SimpleSA strategy of defining noun word boxes as fixed identity gates and are compared with both the baseline SimpleSA ansatz and versions with noun parameterization. The findings reveal that the Rx-CNOT and Rx-CRz models with non-parameterized nouns significantly outperform the SimpleSA ansatz, achieving an accuracy of 96.91%.

DOI
10.1109/access.2025.3596031

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This paper investigates sentiment classification using QNLP within the Variational Quantum Algorithms (VQA) framework, which offers a practical approach for current hardware limitations by optimizing parameterized quantum circuits through classical computation. Designing an effective VQA ansatz is a key challenge because it directly influences the model’s expressiveness and trainability. To address this, a new ansatz called the GeneralQC ansatz is proposed by combining the preliminary SimpleSA ansatz with the circuit-centric quantum classifier, aiming to enhance performance by integrating the strengths of both methods. Three instantiations of the GeneralQC ansatz—H-CRx, Rx-CNOT, and Rx-CRz—are explored under the SimpleSA strategy of defining noun word boxes as fixed identity gates and are compared with both the baseline SimpleSA ansatz and versions with noun parameterization. The findings reveal that the Rx-CNOT and Rx-CRz models with non-parameterized nouns significantly outperform the SimpleSA ansatz, achieving an accuracy of 96.91%.
Sentiment classification is a valuable application of natural language processing that has seen wide usage in optimizing business processes. This paper explores a novel implementation of sentiment analysis using the Variational Quantum Algorithms (VQA) framework. As ansatz choice determines model performance in VQA, this paper proposes an alternative ansatz for the sentiment classification task in quantum representation. Specifically, it builds upon previous work in quantum sentiment classification by proposing an alternative ansatz to the Instantaneous Quantum Polytime ansatz, entitled Simple Sentiment Analysis (SimpleSA) ansatz. A key feature of the SimpleSA ansatz is the decision to neglect noun parameterization. The proposed SimpleSA has less complexity than the other ansätze in terms of number of parameters and number of gates. Moreover, experimental results show that the SimpleSA ansatz with H-CNOT-Rz-H compound block construction outperforms the Instantaneous Quantum Polytime (IQP) ansatz at 85.00% accuracy. Furthermore, SimpleSA optimization converges 20.89% faster than Instantaneous Quantum Polytime (IQP) for the Simultaneous Perturbation Stochastic Approximation (SPSA) method with 130 iterations. The proposed work is useful for applications of quantum computers for sentiment analyses and classifications.
We present a component-wise ablation study of a quantum natural language processing (QNLP) pipeline for sentiment classification, focusing on how classical and quantum design choices interact under NISQ constraints. Starting from a baseline, we evaluate five different qubit numbers, six sentence embedding strategies, two dimensionality reduction methods, two feature encoding schemes, and three variational ansatzes. Each stage is tested independently while fixing previously optimized components, allowing us to isolate the contribution of each. Experiments are conducted on simulated quantum backends. We find that sentence-level transformer embeddings (MiniLM, MPNet), linear dimensionality reduction (PCA), and simple angle encoding provide substantial accuracy gains without additional training cost. Finally, we introduce a shallow custom ansatz that induces a multiplicative cosine kernel across qubits. While the best overall test accuracy(87%)(87 \%)(87%)is achieved using the RealAmplitude ansatz, the custom ansatz closely follows (86.5 %) while reducing runtime by over35%(277 s35 \%(277 ~\mathrm{s}35%(277 svs. 432 s). This demonstrates a promising trade-off between performance and efficiency, supporting the design of scalable QNLP pipelines under limited quantum resources.
Combining variational quantum circuits with classical neural network models is a potential direction for quantum natural language processing (QNLP) in the NISQ (Noisy Intermediate-Scale Quantum) era. We propose a hybrid classical-quantum neural network that enhances a transformer architecture in two key ways: first, by incorporating optimized variational quantum circuits into the self-attention and multilayer perceptron components, and second, by integrating a hybrid convolutional module for effective dimensionality reduction which can reduce the quantum resources required for subsequent layers and enhances the model’s trainability by mitigating issues like the barren plateau phenomenon, thus making it feasible to construct a deeper and more powerful network architecture. Our experiments on three widely-used sentiment analysis datasets demonstrate that the proposed model achieves comparable or even superior performance in terms of accuracy and F1 score compared to classical transformer-based sentiment classification models with similar structures, while reducing the number of parameters through the utilization of quantum advantages.
Quantum Machine Learning (QML) has emerged as a compelling research domain that unites quantum computing principles with classical learning theory to address computationally demanding classification problems.This paper investigates two principal QML architectures-Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN)-within the context of binary classification.We present an end-to-end hybrid quantum-classical pipeline applied to the UCI Mushroom dataset, comprising 22 categorical attributes, and demonstrate that quantumenhanced models can achieve competitive classification performance under present-day hardware constraints.The pipeline integrates Quantum Random Access Code (QRAC) encoding for dimensionality reduction, ZZFeatureMapbased quantum feature mapping, RealAmplitudes parameterized ansatz, and classical optimizer-driven parameter updates using the COBYLA algorithm.The complete system is deployed through a Flask web interface and containerized with Docker for reproducibility.Experimental results on a quantum simulator indicate a classification accuracy of 87.4%, precision of 0.891, and recall of 0.863, establishing the practical viability of NISQ-era quantum classifiers for structured categorical data.The paper further analyzes convergence behavior, circuit depth effects, and comparative performance against classical baselines, providing insights into the strengths and limitations of current QML architectures for real-world classification deployment.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Whether parameterized quantum circuits (PQCs) can be systematically constructed to be both trainable and expressive remains an open question. Highly expressive PQCs often exhibit barren plateaus, while several trainable alternatives admit efficient classical simulation. We address this question by deriving a finite-sample, dimension-independent concentration bound for estimating the variance of a PQC cost function, yielding explicit trainability guarantees. Across commonly used ansätze, we observe an anticorrelation between trainability and expressibility, consistent with theoretical insights. Building on this observation, we propose a property-based ansatz-search framework for identifying circuits that combine trainability and expressibility. We demonstrate its practical viability on a real quantum computer and apply it to variational quantum algorithms. We identify quantum neural network ansätze with improved effective dimension using over 6×6 \times6× fewer parameters, and for VQE on H2\mathrm{H}_2H2​ we achieve UCCSD-like accuracy at substantially reduced circuit complexity.