@mastersthesis{digilib78782,
           month = {August},
           title = {OPTIMASI PENGENALAN KARAKTER ARAB TULISAN TANGAN MENGGUNAKAN MODEL HYBRID QUANTUM CONVOLUTIONAL NEURAL NETWORK (QCNN) DENGAN TEKNIK DENSE ANGLE ENCODING},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 24206051019 Haerul Anam},
            year = {2026},
            note = {Prof. Dr. Ir. Shofwatul 'Uyun, S.T., M.Kom., IPM., ASEAN Eng.},
        keywords = {Dense Angle Encoding, Hybrid QCNN, Karakter Arab, Quantum Machine Learning, Variational Quantum Circuit},
             url = {https://digilib.uin-suka.ac.id/id/eprint/78782/},
        abstract = {Handwritten Arabic character recognition faces challenges due to variations in writing styles and morphological similarities, particularly among homoglyph characters that differ in the number or position of dots. In a Hybrid Quantum Convolutional Neural Network (HQCNN), these challenges are also related to limitations in feature representation when latent CNN features are mapped to a quantum circuit. Standard Angle Encoding maps one feature to one qubit, causing the required number of qubits to increase with the feature dimension. This study aims to develop and evaluate a Hybrid Quantum Convolutional Neural Network (HQCNN) with Dense Angle Encoding (DAE) for handwritten Arabic character recognition and to analyze the effect of}
}