Most of the quantum error correction methods are symmetric. Symmetric methods are implemented by considering the amplitude of bit flip(X) and phase flip(Z) errors as same. With the quantum ...experiments, it is observed that the amplitude of Z errors are more compared to X errors. Due to which the need of asymmetric error correction has increased. This paved a path for the development of asymmetric error correction methods. In this paper, we discussed the concept of asymmetric quantum error correction (AQEC) and proposed an efficient approach for AQEC with encoding, syndrome measurement and decoding operations with increased fidelity to 85.89% and reduced circuit depth to 48%.
The traditional approach for analyzing the quality of arecanuts is based on their external appearance. However, using machine learning and deep learning techniques, automated classifications were ...performed. But the true quality can only be analyzed when the internal structure of the arecanut is analyzed. Therefore, we use the X-ray imaging technique to determine the internal quality of arecanuts. We prepared a novel dataset of arecanut X-ray images and used a YOLOv5 based deep learning architecture for classification. The present study employs an adaptive genetic algorithm based approach for hyperparameter optimization to enhance the mean average precision (mAP) using a light weight model that is generated using a ghost network and a feature pyramid network (FPN). We achieved the highest mAP of 97.84% using our method with a lower model size of 15 MB. Our method has excelled in detection performance when compared to cutting-edge object detection algorithms such as YOLOv3, YOLOv4, Detetron, YOLOv6, YOLOv8, and YOLOX. We also acknowledged the performance enhancement using the adaptive genetic algorithm on the Pascal VOC 2007 image dataset. Despite significant computational requirements for executing genetic algorithms, we showed that genetic algorithms can be used to boost mAP. Additionally, the methodology developed in this investigation produced multiple models with the best mAP featuring optimized hyperparameters. This methodical strategy is useful for the design of an automatic, non-destructive, integrated X-ray image based classification system. This system has the potential to revolutionize the quality assessment of arecanuts by offering a more efficient method of evaluation.
The advancement in education has emphasized the need to evaluate the quality of the examination questions and the cognitive levels of students. Many educational institutions now acknowledge Bloom's ...taxonomy-based students' cognitive levels evaluating subject-related learning. Therefore, in this paper, a novel optimized Examination Question Classification framework, referred to as QC-DcCapsGANAOSA, is proposed by combining the Dual-channel Capsule generative Adversarial Network (DcCapsGAN) with Atomic Orbital Search Algorithm (AOSA) for preprocessing a real-time online dataset of university examination questions, thus identify the key features from the raw data using Term Frequency Inverse Document Frequency (TF-IDF) and finally classifying the examination questions. Atomic Orbital Search Algorithm is used to fine-tune the parameters' weights of the DcCapsGAN, and then uses these weights to categorize questions as Knowledge Level, Comprehension Level, Application Level, Analysis Level, Synthesis Level, and Evaluation Level. Experimental results demonstrate the superiority of the proposed method (QC-DuCapsGAN-AOSA) when compared to the state-of-the-art methods such as QC-LSTM-CNN and QC-BiGRU-CNN with an accuracy improvement of 23.65% and 29.04%, respectively.
The importance of reversible operations has been increasing day by day to overcome the drawbacks of irreversible computation. Quantum computers perform operations exponentially faster by taking ...advantage of reversible operations. Reversible operations play an essential role in developing energy and cost-efficient circuits. The efficiency of a quantum circuit is measured in terms of Quantum cost and Quantum depth. In this paper, we propose an optimization algorithm for Entanglement-based Quantum error correction, which plays a crucial role in various applications like quantum teleportation, secure communications, quantum key distribution, etc. We performed the experiments using Qiskit and RCViewer+ tools. Qiskit tool is used to run the quantum algorithms and measure the quantum depth; the RCViewer+ tool is used to measure the quantum cost. The proposed algorithm optimizes the quantum cost and depth compared to the existing approaches.
Quantum Key Distribution is a major building block of Quantum cryptography. A Quantum key is generated through the quantum particles. Quantum particles perform operations based on quantum mechanical ...principles like superposition and entanglement. With these principles, it is not possible for a third party to observe the quantum information. This proves that the Quantum key is highly secured and unbreakable compared to the current existing classical keys. But the major problem observed with quantum information is inbuilt noise which results in high error rates. To overcome this problem, we propose a Quantum key distribution protocol with entanglement purification and asymmetric quantum error correction. With the experiment, we have observed that the proposed method reduces the third-party quantum key detection probability and also increases the quantum key length and communication efficiency by reducing the error rate to 0.04%.
Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing ...power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)‐based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum‐Classical Convolutional Neural Network (HQC‐CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q‐Means++ Clustering for segmenting the images and a Max‐cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max‐cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology.
Abstract Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural ...network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.
With the ever-increasing number of Internet users in this digital age, exposure to malicious attacks is increasing. Every day, large volumes of malicious content are generated to exploit 0-day ...vulnerabilities. There is every possibility of downloading malicious files unintentionally, which could corrupt the system and user data. With the advancements in technology and growing dependence on digital data, malicious software detection has become a crucial task. The existing approaches need modifications to support and detect the latest attacks. Recently, artificial intelligence-based malicious file detection methods have been proposed. In the past, most of the works analyzed the executable file features and visual features from their corresponding images independently. Additionally, image-based analysis has been exploited for categorical classification, i.e., finding the family once it is known to be malware. We propose a CNN-based model that extracts visual features from malware images, which outperforms existing approaches on a benchmark dataset like MalImg. We study the effect of using a hybrid feature set containing these visual features integrated with statically obtained opcode frequencies for the detection of malware. Our experiments on standard datasets demonstrate that there is no significant performance improvement using this hybrid approach.
In a DNS Tunneling attack, data or other useful information is embedded within a DNS query and exfiltrated. Such attacks are difficult to detect because DNS is a fundamental protocol and blocking ...legitimate domain names can lead to an unpleasant experience for the users. Thus, detecting whether the DNS query is exfiltrating data or not is a challenging task. Mimicking genuine queries by the attacker makes this even more difficult. This research work presents two different methods for detecting the DNS Tunneling query and later they are combined to build a DNS Tunneling Attack Detector that can inform the client about a potential attack going on in real time. The first method uses cache misses in a DNS cache server and the second method utilizes machine learning techniques to classify a given DNS query. Overall, with around 93% accuracy of certain Machine Learning classifiers on classifying on a per packet basis along with extra validation from the cache-miss approach, a detector has been developed to accurately report DNS tunneling traffic
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