• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.51-60
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• 2023

Quantum machine learning (QML) is an area of quantum computing that leverages its principles to develop machine learning algorithms and techniques. QML is aimed at combining traditional machine learning with the capabilities of quantum computing to devise approaches for problem solving and (big) data processing. Nevertheless, QML is in its early stage of the research and development. Thus, more theoretical studies are needed to understand whether a significant quantum speedup can be achieved compared with classical machine learning. If this is the case, the underlying physical principles may be explained. First, fundamental concepts and elements of QML should be established. We describe the inception and development of QML, highlighting essential quantum computing algorithms that are integral to QML. The advent of the noisy intermediate-scale quantum era and Google's demonstration of quantum supremacy are then addressed. Finally, we briefly discuss research prospects for QML.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1674-1688
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• 2023

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.3
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• pp.29-44
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• 2021

The study provides a quantitative and qualitative description of global research in the domain of quantum machine learning (QML) as a way to understand the status of global research in the subject at the global, national, institutional, and individual author level. The data for the study was sourced from the Scopus database for the period 1999-2020. The study analyzed global research output (1374 publications) and global citations (22434 citations) to measure research productivity and performance on metrics. In addition, the study carried out bibliometric mapping of the literature to visually represent network relationship between key countries, institutions, authors, and significant keyword in QML research. The study finds that the USA and China lead the world ranking in QML research, accounting for 32.46% and 22.56% share respectively in the global output. The top 25 global organizations and authors lead with 35.52% and 16.59% global share respectively. The study also tracks key research areas, key global players, most significant keywords, and most productive source journals. The study observes that QML research is gradually emerging as an interdisciplinary area of research in computer science, but the body of its literature that has appeared so far is very small and insignificant even though 22 years have passed since the appearance of its first publication. Certainly, QML as a research subject at present is at a nascent stage of its development.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.34-50
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• 2023

Quantum error correction is a key technology for achieving fault-tolerant quantum computation. Finding the best decoding solution to a single error syndrome pattern counteracting multiple errors is an NP-hard problem. Consequently, error decoding is one of the most expensive processes to protect the information in a logical qubit. Recent research on quantum error decoding has been focused on developing conventional and neural-network-based decoding algorithms to satisfy accuracy, speed, and scalability requirements. Although conventional decoding methods have notably improved accuracy in short codes, they face many challenges regarding speed and scalability in long codes. To overcome such problems, machine learning has been extensively applied to neural-network-based error decoding with meaningful results. Nevertheless, when using neural-network-based decoders alone, the learning cost grows exponentially with the code size. To prevent this problem, hierarchical error decoding has been devised by combining conventional and neural-network-based decoders. In addition, research on quantum error decoding is aimed at reducing the spacetime decoding cost and solving the backlog problem caused by decoding delays when using hardware-implemented decoders in cryogenic environments. We review the latest research trends in decoders for quantum error correction with high accuracy, neural-network-based quantum error decoders with high speed and scalability, and hardware-based quantum error decoders implemented in real qubit operating environments.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.644-652
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• 2022

During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

• Current Optics and Photonics
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• v.6 no.1
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• pp.92-103
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• 2022

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1267-1276
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• 2024

The emerging technologies at the industrial level have deployed rapidly within the energy transition process innovations. The nuclear industry incorporates several technologies like Artificial Intelligence (AI), Machine Learning (ML), Digital Twins, High-Performance-Computing (HPC) and Quantum Computing (QC), among others. Factors identifications are explained to set up a regulatory framework in the digitalization era, providing new capabilities paths for nuclear technologies in the forthcoming years. The Analytical Network Process (ANP) integrates the quantitative-qualitative decision-making analysis to assess the implementation of different aspects in the digital transformation for the New-Energy Transition Era (NETE) with a Nuclear Power Infrastructure Development (NPID).

• Food Science of Animal Resources
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• v.41 no.2
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• pp.312-323
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• 2021

The purpose of this study was to use 1H nuclear magnetic resonance (1H NMR) to quantify taste-active and bioactive compounds in chicken breasts and thighs from Korean native chicken (KNC) [newly developed KNCs (KNC-A, -C, and -D) and commercial KNC-H] and white-semi broiler (WSB) used in Samgye. Further, each breed was differentiated using multivariate analyses, including a machine learning algorithm designed to use metabolic information from each type of chicken obtained using 1H-13C heteronuclear single quantum coherence (2D NMR). Breast meat from KNC-D chickens were superior to those of conventional KNC-H and WSB chickens in terms of both taste-active and bioactive compounds. In the multivariate analysis, meat portions (breast and thigh) and chicken breeds (KNCs and WSB) could be clearly distinguished based on the outcomes of the principal component analysis and partial least square-discriminant analysis (R2=0.945; Q2=0.901). Based on this, we determined the receiver operating characteristic (ROC) curve for each of these components. AUC analysis identified 10 features which could be consistently applied to distinguish between all KNCs and WSB chickens in both breast (0.988) and thigh (1.000) meat without error. Here, both 1H NMR and 2D NMR could successfully quantify various target metabolites which could be used to distinguish between different chicken breeds based on their metabolic profile.