The rapid advancement of machine learning has driven the exploration of various computational models to address classification problems across datasets with diverse characteristics. This study aimed to compare the performance of Artificial Neural Network (ANN) and Hybrid Quantum Neural Network (Hybrid QNN) under controlled experimental conditions. Three benchmark datasets, namely Red Wine Quality, Banknote Authentication, and Leukemia Gene Expression, were used with a training and test data split of 80:20. The evaluation was conducted using accuracy, precision, recall, F1-score, and computational time. The results showed that on the Gene Expression Leukemia dataset, Hybrid QNN achieved an accuracy of 0.9333, significantly outperforming ANN, which obtained 0.6111. Conversely, ANN demonstrated competitive performance and higher computational efficiency on low- and medium-dimensional datasets. These findings indicate that the advantages of Hybrid QNN are contextual and strongly dependent on data characteristics.Keywords: Artificial Neural Network; Hybrid Quantum Neural Network; classification; machine learningAbstrakPerkembangan pesat pembelajaran mesin telah mendorong eksplorasi berbagai model komputasi untuk menyelesaikan permasalahan klasifikasi pada data dengan karakteristik yang beragam. Penelitian ini bertujuan untuk membandingkan kinerja Artificial Neural Network (ANN) dan Hybrid Quantum Neural Network (Hybrid QNN) dalam kondisi eksperimen yang terkontrol. Tiga dataset acuan digunakan, yaitu Red Wine Quality, Banknote Authentication, dan Gene Expression Leukemia, dengan pembagian data latih dan uji sebesar 80:20. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, F1-score, serta waktu komputasi. Hasil eksperimen menunjukkan bahwa pada dataset Gene Expression Leukemia, Hybrid QNN mencapai akurasi 0,9333, jauh lebih tinggi dibandingkan ANN sebesar 0,6111. Sebaliknya, ANN menunjukkan performa yang kompetitif dan lebih efisien pada dataset berdimensi rendah hingga menengah. Temuan ini menunjukkan bahwa keunggulan Hybrid QNN bersifat kontekstual dan bergantung pada karakteristik data.
The rapid advancement of machine learning has driven the exploration of various computational models to address classification problems across datasets with diverse characteristics. This study aimed to compare the performance of Artificial Neural Network (ANN) and Hybrid Quantum Neural Network (Hybrid QNN) under controlled experimental conditions. Three benchmark datasets, namely Red Wine Quality, Banknote Authentication, and Leukemia Gene Expression, were used with a training and test data split of 80:20. The evaluation was conducted using accuracy, precision, recall, F1-score, and computational time. The results showed that on the Gene Expression Leukemia dataset, Hybrid QNN achieved an accuracy of 0.9333, significantly outperforming ANN, which obtained 0.6111. Conversely, ANN demonstrated competitive performance and higher computational efficiency on low- and medium-dimensional datasets. These findings indicate that the advantages of Hybrid QNN are contextual and strongly dependent on data characteristics.Keywords: Artificial Neural Network; Hybrid Quantum Neural Network; classification; machine learningAbstrakPerkembangan pesat pembelajaran mesin telah mendorong eksplorasi berbagai model komputasi untuk menyelesaikan permasalahan klasifikasi pada data dengan karakteristik yang beragam. Penelitian ini bertujuan untuk membandingkan kinerja Artificial Neural Network (ANN) dan Hybrid Quantum Neural Network (Hybrid QNN) dalam kondisi eksperimen yang terkontrol. Tiga dataset acuan digunakan, yaitu Red Wine Quality, Banknote Authentication, dan Gene Expression Leukemia, dengan pembagian data latih dan uji sebesar 80:20. Evaluasi dilakukan menggunakan metrik akurasi, precision, recall, F1-score, serta waktu komputasi. Hasil eksperimen menunjukkan bahwa pada dataset Gene Expression Leukemia, Hybrid QNN mencapai akurasi 0,9333, jauh lebih tinggi dibandingkan ANN sebesar 0,6111. Sebaliknya, ANN menunjukkan performa yang kompetitif dan lebih efisien pada dataset berdimensi rendah hingga menengah. Temuan ini menunjukkan bahwa keunggulan Hybrid QNN bersifat kontekstual dan bergantung pada karakteristik data.
Classifying product reviews on e-commerce platforms, both real and fake, requires a model that can effectively represent text data patterns. This study aims to compare the performance of several Quantum Machine Learning methods, namely QNN, QSVC, and Hybrid Quantum kernel and Classical SVM, in classifying Amazon product reviews. The study uses a quantitative approach with a computational experimental design. Review data is represented using TF-IDF, standardized, and reduced in dimension with Principal Component Analysis (PCA) before being transformed into quantum feature space. Performance evaluation is carried out using accuracy, precision, recall, F1-Score, and MCC metrics. The experimental results show that QNN achieved the best performance with an accuracy value of 85,63%, an F1-Score of 0.9130, and an MCC of 0.5608, while QSVC and the hybrid approach achieved an accuracy of 83.23% with an MCC of 0.4331. These results indicate that QNN has more balanced classification performance.Keywords: Quantum Neural Network; Fake Review Detection; Amazon Reviews; Natural Language Processing; Quantum Machine Learning. AbstrakKlasifikasi ulasan produk pada platform e-commerce, baik ulasan asli maupun palsu, memerlukan model yang mampu merepresentasikan pola data teks secara efektif. Penelitian ini bertujuan untuk membandingkan kinerja beberapa metode Quantum Machine Learning (QML), yaitu QNN, Quantum Support Vector (QSVC), dan Hybrid Quantum kernel and Classical SVM, dalam mengklasifikasikan ulasan produk Amazon. Penelitian menggunakan pendekatan kuantitatif dengan desain eksperimen komputasional. Data ulasan direpresentasikan menggunakan TF-IDF, distandardisasi, dan direduksi dimensinya dengan Principal Component Analysis (PCA) sebelum ditransformasikan ke ruang fitur kuantum. Evaluasi kinerja dilakukan menggunakan metrik accuracy, precision, recall, F1-Score, dan MCC. Hasil eksperimen menunjukkan bahwa QNN memperoleh kinerja terbaik dengan nilai accuracy sebesar 85,63%, F1-Score 0.9130, dan MCC 0.5043, sedangkan QSVC dan pendekatan hybrid mencapai accuracy 83,23% dengan MCC 0,4331. Hasil ini menunjukkan bahwa QNN memiliki performa klasifikasi yang lebih seimbang.
Tantangan utama dalam penggunaan CNN konvensional untuk klasifikasi citra medis terletak pada tingginya kompleksitas parameter dan potensi penurunan efisiensi generalisasi pada volume data besar. Penelitian ini mengeksplorasi solusi alternatif melalui arsitektur hibrida, yaitu Hybrid Quantum-Classical Neural Network (HQCNN) yang diterapkan pada dataset PathMNIST. Dalam skema ini, lapisan CNN klasik difungsikan sebagai ekstraktor fitur spasial, yang kemudian dipetakan ke dalam domain kuantum melalui mekanisme angle encoding pada konfigurasi 4 qubit. Kebaruan dalam studi ini terletak pada pengujian skalabilitas model yang dilakukan secara bertahap hingga mencapai volume 130.000 sampel data. Hasil eksperimen mengonfirmasi keunggulan HQCNN dengan capaian akurasi sebesar 83,0%, melampaui performa model CNN klasik yang mengalami penurunan efektivitas signifikan pada angka 72,3%. Temuan tersebut menunjukkan bahwa integrasi prinsip kuantum seperti entanglement melalui gerbang CNOT mampu menghasilkan pemisahan kelas yang lebih tajam dan meminimalisir risiko overfitting pada dataset medis berskala besar.
This study analyzes the comparative performance of Quantum Machine Learning in classifying factual and hoax political news using three approaches, namely Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), and Hybrid Quantum Kernel with Classical SVM. News data is represented using TF-IDF and its dimensionality is reduced using Principal Component Analysis, then balanced using SMOTE. Feature transformation is carried out to the quantum domain through angle encoding, then applied to the QML model. Performance evaluation is carried out using accuracy, precision, recall, and F1-Score. The experimental results show that QSVC has the best performance with an accuracy of 0.629 and an F1-Score of 0.735, followed by QNN and Hybrid Quantum Kernel Classical SVM. This study proves that the quantum kernel-based approach is effective in classifying medium-dimensional text, while also demonstrating the potential of Quantum Machine Learning as an alternative method for classifying factual and hoax political news.Keywords: Quantum Machine Learning; Quantum Neural Network; Quantum Support Vector Classifier; Hybrid Quantum Kernel; News Classification AbstrakPenelitian ini menganalisis perbandingan kinerja Quantum Machine Learning dalam klasifikasi berita politik fakta dan hoaks dengan menggunakan tiga pendekatan, yaitu Quantum Neural Network (QNN), Quantum Support Vector Classifier (QSVC), dan Hybrid Quantum Kernel dengan Classical SVM. Data berita direpresentasikan menggunakan TF-IDF dan direduksi dimensinya dengan Principal Component Analysis, kemudian diseimbangkan menggunakan SMOTE. Transformasi fitur dilakukan ke domain kuantum melalui angle encoding, kemudian diterapkan pada model QML. Evaluasi kinerja dilakukan menggunakan accuracy, precision, recall, dan F1-Score. Hasil eksperimen menunjukkan QSVC memiliki performa terbaik dengan accuracy 0,629 dan F1-Score 0,735, diikuti QNN dan Hybrid Quantum Kernel Classical SVM. Penelitian ini membuktikan bahwa pendekatan berbasis quantum kernel efektif dalam klasifikasi teks berdimensi sedang, sekaligus menunjukkan potensi Quantum Machine Learning sebagai alternatif metode klasifikasi berita politik fakta dan hoaks.
Percepatan penemuan material baru terkendala oleh tingginya biaya komputasi metode kimia quantum konvensional seperti DFT (Density Functional Theory). Meskipun metode Deep Learning klasik menawarkan alternatif yang lebih cepat, pendekatan ini sering kali membutuhkan arsitektur dengan jutaan parameter untuk mencapai akurasi yang memadai. Penelitian ini mengevaluasi potensi Quantum Machine Learning (QML) untuk mengatasi masalah inefisiensi parameter tersebut. Secara spesifik, penelitian ini membandingkan kinerja model Hybrid Quantum Neural Network (HQNN) berbasis sirkuit variasional 4-qubit melawan model Classical Multi-Layer Perceptron (MLP) sebagai baseline pada tugas regresi energi internal (U0) menggunakan dataset QM9. Untuk memastikan perbandingan yang adil, kedua model dibatasi secara ketat memiliki jumlah parameter yang identik, yaitu sekitar 72 parameter. Hasil eksperimen menunjukkan bahwa model Quantum mencapai konvergensi pelatihan yang lebih baik dengan nilai Mean Squared Error (MSE) akhir sebesar 0.6292, mengungguli model Klasik yang berhenti di angka 0.7369. Lebih lanjut, model Quantum mencatatkan akurasi prediksi dengan Mean Absolute Error (MAE) sebesar 759.89 Hartree. Temuan ini membuktikan bahwa fitur quantum entanglement memberikan daya ekspresi (expressibility) yang superior dibandingkan transformasi linear klasik pada batasan sumber daya komputasi yang ketat, menjadikan algoritma hibrida quantum solusi yang menjanjikan untuk simulasi kimia di era Noisy Intermediate-Scale Quantum (NISQ).
Breast cancer is a major health concern, and early detection can make a great difference in treatment and survival rates for breast cancer patients. Machine learning methods have noticeably improved prediction accuracy on high-dimensional medical datasets and have become widely used tools in medical diagnostics. The transition to a quantum computational framework has opened new directions in machine learning research. By using qubits instead of classical bits, quantum models may provide new ways to represent and process complex medical data. In medical diagnostics, this has led to a growing interest in whether quantum approaches can improve classification performance more effectively than classical methods. The purpose of this paper is to compare classical and quantum machine learning models on the task of breast cancer classification and to determine whether quantum models can achieve higher accuracy and faster prediction on a selected dataset. Alongside the main simulator-based experiment, a smaller experiment was performed on a real IBM quantum computer to demonstrate the practical execution of the same task under current hardware constraints. In the practical part of the study, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used. This dataset consists of 569 samples with 30 numerical features extracted from digitized fine needle aspirate images and provides a reliable basis for evaluating model performance. A comparative analysis was conducted between classical and quantum machine learning models, including a support vector machine (SVM), an artificial neural network (ANN), a quantum support vector machine (QSVM), and a hybrid quantum-classical neural network (QNN). In the simulator-based experiment, both SVM and ANN achieved an accuracy of 0.956 with an F1-score of 0.965 on the test set, while QSVM reached an accuracy of 0.807 with an F1-score of 0.866 and the Hybrid QNN achieved 0.623 accuracy with an F1-score of 0.677. These quantum and hybrid models also required substantially longer training times than the classical baselines. A small hardware experiment on an IBM quantum device further illustrates both the practical feasibility and the current limitations of executing this classification task on noisy intermediate-scale quantum hardware.
Machine learning (ML), which has been increasingly applied to complex problems such as catalyst development, encounters challenges in data collection and structuring. Quantum neural networks (QNNs) outperform classical ML models, such as artificial neural networks (ANNs), in prediction accuracy, even with limited data. However, QNNs have limited available qubits. To address this issue, we introduce a hybrid QNN model, combining a parametrized quantum circuit with an ANN structure. We used the catalyst data sets of the dry reforming of methane reaction from the literature and in-house experimental results to compare the hybrid QNN and the ANN models. The hybrid QNN exhibited superior prediction accuracy and a faster convergence rate, achieving an R 2 of 0.942 at 2478 epochs, whereas the ANN achieved an R 2 of 0.935 at 3175 epochs. For the 224 in-house experimental data points previously unreported in the literature, the hybrid QNN exhibited an enhanced generalization performance. It showed a mean absolute error (MAE) of 13.42, compared with an MAE of 27.40 for the ANN under similar training conditions. This study highlights the potential of the hybrid QNN as a powerful tool for solving complex problems in catalysis and chemistry, demonstrating its advantages over classical ML models.