Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah IAES Nawala dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar mengenai algoritma kuantum. Algoritma kuantum dirancang secara khusus untuk menyelesaikan masalah komputasi secara lebih efisien daripada algoritme klasik. Shah dkk. (2023) mengkombinasikan algoritma kuantum dengan deep learning. Mereka mengembangkan teknik pengenalan senjata untuk kejahatan jalanan menggunakan deep learning dan quantum deep learning (street-crimes modelled arms recognition technique employing deep learning and quantum deep learning, SMARTED). Teknik SMARTED menggunakan deep learning dan quantum deep learning untuk mendeteksi senjata secara real-time. Tujuannya adalah untuk meningkatkan efisiensi pendeteksian senjata dalam kamera, video, atau gambar CCTV dan secara otomatis memberi tahu orang yang bersangkutan untuk mengambil tindakan yang diperlukan. SMARTED menggunakan RetinaNet untuk mendeteksi senjata secara real-time dengan akurasi rata-rata 90%. Selain itu, mereka juga mengimplementasikan teknologi komputasi kuantum untuk mengembangkan RetinaNet, yang terinspirasi oleh kuantum (QIR-Net), untuk meningkatkan keandalan yang signifikan.
An increase in population causes loopholes in controlling law and order situations. One of the threatening aspects of peace is the availability of weapons to the general public. As a result, many dangerous situations arise, most notably street crimes. Traditional methods are not sufficient to deal with such situations. Consequently, the police and other security concerns need serious technological reforms to prevent such situations. In modern technology tools, deep learning has made great improvements in various areas of daily life, especially in object detection. This paper presents an efficient technique for detecting weapons from closed-circuit television (CCTV) cameras, videos, or images. Upon the detection of the weapon, the concerned person is automatically informed to take the necessary action; without human intervention. For the first time, RetinaNet has been employed to detect weapons in real-time scenarios. RetinaNet has shown remarkable improvement in this domain, by achieving an average of 90% accuracy in real-time scenarios. With the emergence of quantum computing, many computer environments saw a revolution. Thus, we have also utilized quantum computing technology for real-time weapons detection using quantum deep learning. In this paper, quantum inspired RetinaNet (QIR-Net) is developed for weapons detection and amazing results are observed.
Street-crimes modelled arms recognition technique employing deep learning and quantum deep learning (SMARTED)
Syed Atif Ali Shah, Ahmed Abdel-Wahab, Nasir Ageelani, Najeeb Najeeb
Subbiah dkk. (2023) mengembangkan quantum transfer learning untuk klasifikasi gambar adalah teknik yang memanfaatkan kekuatan komputasi kuantum untuk meningkatkan kinerja model deep learning dalam tugas ini. Pendekatan ini menggunakan model yang terinspirasi oleh kuantum, seperti quantum-inspired RetinaNet (QIR-Net), yang telah menunjukkan hasil yang signifikan dalam pendeteksian senjata. Quantum transfer learning juga memiliki potensi untuk diterapkan di dunia nyata, seperti sistem keamanan dan pengawasan, yang membutuhkan klasifikasi gambar yang akurat dan efisien.
Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.
Quantum transfer learning for image classification
Geetha Subbiah, Shridevi S. Krishnakumar, Nitin Asthana, Prasanalakshmi Balaji, Thavavel Vaiyapuri
Peran algoritma kuantum juga digunakan oleh Daniel dkk. (2024) untuk membaca tulisan tangan. Pengenalan huruf tulisan tangan merupakan task yang kompleks karena adanya perbedaan besar dalam gaya penulisan setiap orang dan adanya berbagai artefak gambar seperti keburaman, variasi intensitas, dan noise. Metode yang diusulkan untuk mengatasi keterbatasan ini adalah dengan menggabungkan algoritma jaringan saraf tiruan kuantum (quantum artificial neural network, QCNN). QCNN mampu melakukan operasi yang lebih kompleks daripada CNN klasik dan dapat mencapai tingkat akurasi yang lebih tinggi, terutama ketika bekerja dengan data yang berisik atau tidak lengkap. Efektivitas model yang diusulkan ditunjukkan pada dataset National Institute of Standards and Technology (MNIST) yang telah dimodifikasi dan mencapai akurasi rata-rata 91,08%.
The recognition of handwritten digits holds a significant place in the field of information processing. Recognizing such characters accurately from images is a complex task because of the vast differences in people’s writing styles. Furthermore, the presence of various image artifacts such as blurring, intensity variations, and noise adds to the complexity of this process. The existing algorithm, convolution neural network (CNN) is one of the prominent algorithms in deep learning to handle the above problems. But there is a difficulty in handling input data that differs significantly from the training data, leading to decreased accuracy and performance. In this work, a method is proposed to overcome the aforementioned limitations by incorporating a quantum convolutional neural network algorithm (QCNN). QCNN is capable of performing more complex operations than classical CNNs. It can achieve higher levels of accuracy than classical CNNs, especially when working with noisy or incomplete data. It has the potential to scale more efficiently and effectively than classical CNNs, making them better suited for large-scale applications. The effectiveness of the proposed model is demonstrated on the modified national institute of standards and technology (MNIST) dataset and achieved an average accuracy of 91.08%.
Handwritten digit recognition using quantum convolution neural network
Ravuri Daniel, Bode Prasad, Prudhvi Kiran Pasam, Dorababu Sudarsa, Ambarapu Sudhakar, Bodapati Venkata Rajanna
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai peran algoritma kuantum. Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman dan membaca artikel secara GRATIS melalui tautan-tautan berikut: https://ijeecs.iaescore.com/, http://telkomnika.uad.ac.id/, dan https://ijai.iaescore.com/.
Redaksi: I. Busthomi