In this research, a quantum computational approach was employed to enhance the Adaline and Hebbian algorithms. A comparative analysis of these algorithms was conducted, focusing on their performance, specifically the accuracy of test outcomes. The investigation was carried out utilizing a hepatitis prediction dataset comprising data related to individuals diagnosed with hepatitis, with observations on whether they were alive or deceased. The dataset encompassed 19 distinctive symptoms, with 18 symptoms utilized for hepatitis pattern recognition and ten symptoms employed as simulated test data for the Adaline and Hebbian algorithms integrated with quantum computation methodologies. The findings of the study revealed advancements in the Adaline and Hebbian algorithms, as influenced by the integration of a quantum computational framework. Notably, the simulation testing outcomes exhibited a remarkable accuracy rate of 100% for both the Adaline and Hebbian algorithms. Consequently, the results underscore the comparable performance of the two algorithms, highlighting their identical accuracy levels.
In this research, a quantum computational approach was employed to enhance the Adaline and Hebbian algorithms. A comparative analysis of these algorithms was conducted, focusing on their performance, specifically the accuracy of test outcomes. The investigation was carried out utilizing a hepatitis prediction dataset comprising data related to individuals diagnosed with hepatitis, with observations on whether they were alive or deceased. The dataset encompassed 19 distinctive symptoms, with 18 symptoms utilized for hepatitis pattern recognition and ten symptoms employed as simulated test data for the Adaline and Hebbian algorithms integrated with quantum computation methodologies. The findings of the study revealed advancements in the Adaline and Hebbian algorithms, as influenced by the integration of a quantum computational framework. Notably, the simulation testing outcomes exhibited a remarkable accuracy rate of 100% for both the Adaline and Hebbian algorithms. Consequently, the results underscore the comparable performance of the two algorithms, highlighting their identical accuracy levels.
This research focuses on developing the MADALINE algorithm using quantum computing. Quantum computing uses binary numbers 0 or 1 or a combination of 0 and 1. The main problem in this research is how to find other alternatives to the MADALINE algorithm to solve pattern recognition problems with a quantum computing approach. The data used in this study are heart failure data to predict whether a patient is at risk of death. The data source comes from KAGGLE, consisting of 299 data with 12 symptoms and one target, alive or dead. The result of this study is an alternative to the MADALINE algorithm that uses quantum computing. The precision of the test results with MADALINE with a learning rate of 0.1 = 100% with 2 epochs. The accuracy of the test results using a quantum approach with a learning rate of 0.1 is 85.71%. The results of this study can be an alternative to the MADALINE algorithm with a quantum computing approach, although it has not shown better accuracy than the classical MADALINE algorithm. More research is needed to produce better accuracy with larger data.
Biological data presents a rich and complex landscape for pattern recognition, offering a unique set of features that are often overlooked by traditional machine learning algorithms. This paper explores the potential of integrating biological information analysis with quantum computing to develop a novel pattern recognition algorithm. We propose a framework leveraging quantum entanglement and quantum algorithms to enhance the efficiency and accuracy of pattern identification and classification across diverse biological datasets. The core claim centers on the design of an algorithm that exploits the unique quantum properties of biological data to significantly outperform existing approaches. This work investigates the theoretical and practical implications of this integration, highlighting the potential for breakthroughs in this rapidly evolving field.
This paper explores the potential of quantum computing to revolutionize pattern recognition, specifically focusing on enhancing accuracy in high-dimensional data. We introduce a novel quantum algorithm, the 'quantum superposition search,' designed to leverage quantum superposition to overcome limitations of classical methods. This research investigates the theoretical and practical implications of applying quantum principles to pattern identification, aiming to demonstrate improved performance across a range of complex datasets. The paper details the algorithm's architecture, its mathematical underpinnings, and preliminary experimental results, highlighting the potential for significant advancements in data analysis and pattern extraction.
The integration of quantum computing principles with machine learning algorithms presents a paradigm shift in addressing complex pattern recognition challenges in video sequences. This research investigates the application of quantum machine learning (QML) algorithms for temporal pattern recognition in video data, comparing their performance against classical approaches across multiple benchmarks. We implement and evaluate quantum neural networks (QNNs), quantum support vector machines (QSVMs), and hybrid quantum-classical architectures using the IBM Quantum Experience platform and Qiskit framework. Our experimental evaluation, conducted on standardized video datasets including UCF-101 and HMDB-51, demonstrates that while quantum algorithms show promising theoretical advantages in feature space dimensionality and processing parallelism, their practical performance remains constrained by current hardware limitations and noise characteristics. Specifically, our hybrid quantum-classical approach achieved 82.7% accuracy on video action recognition tasks, compared to 87.3% for classical SVMs, with quantum advantages emerging in scenarios requiring high-dimensional feature processing. This work contributes to the understanding of quantum computing's potential in multimedia analysis and provides empirical evidence for future quantum machine learning research directions. Keywords: quantum machine learning, pattern recognition, video analysis, quantum neural networks, quantumcomputing
Currently, pattern recognition is an extremely important activity in several fields.Approaches using artificial intelligence are one of the means of detecting these patterns and, therefore, optimizations in these methods are essential for such recognition to be carried out efficiently, both in processing time and in the hit rate.The field of machine learning, one of the paradigms of artificial intelligence, dates back to the beginnings of modern computing and in recent decades has been used intensively in pattern recognition.Despite the technical challenges related to the hardware, quantum computing has also been studied for a better performance of multiple computational tasks that are often unfeasible to be performed by classical algorithms.In this context, one of the fields that gain focus is the use of quantum circuits in machine learning algorithms.Thus, this work aims to compare the performance and complexity of classical algorithms, used in machine learning, to their quantum and hybrid versions, in order to identify the most efficient implementations.Another aspect to highlight is the feasibility of applying such models at certain scales, taking into account the limitations of current hardware.This will be done through a review of current academic production, followed by the development, implementation, testing, and analysis of the performance and complexity of both approaches.It is expected with this experiment that it will be possible to verify the effective possibility of using machine learning algorithms in their hybrid versions, either in current cases or in the near future.
This study aims to leverage the advanced capabilities of quantum computing to construct an efficient framework for processing large-scale health data, uncover potential higher-order correlations in medicine, and enhance the accuracy of smart healthcare diagnosis and treatment. A data processing framework is developed using quantum annealing algorithms and quantum circuits. We call it the quantum medical data simulation computational model (Q-MDSC). A unique encoding method based on quantum bits is employed for health data features, such as encoding symptom information from electronic health records into different quantum bits and representing different alleles of genetic data through superposition states of quantum bits. The properties of quantum entanglement are utilized to relate different data types, and quantum parallelism is harnessed to process multiple data combinations simultaneously. Additionally, this quantum computing framework is compared with traditional data mining methods using the same datasets, which include the Cochrane Systematic Review Database (https://www.cochranelibrary.com), the BioASQ Dataset (https://participants-area.bioasq.org), the PubMed Central Dataset (https://www.ncbi.nlm.nih.gov/pmc), and the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). The datasets are divided into training and testing sets in a 7:3 ratio during the experiments. Tests are conducted on association mining tasks of varying data scales and complexities, ranging from simple symptom-disease associations to complex gene-symptom-disease higher-order associations. The results indicate that, when processing large-scale data, the quantum computing framework improves overall computational speed by approximately 45% compared to traditional algorithms. Regarding uncovering higher-order correlations, the quantum computing framework enhances accuracy by about 30% relative to traditional algorithms. For early disease prediction, the accuracy achieved with the new framework is approximately 25% higher than that of conventional methods. Furthermore, for personalized treatment plan matching, the matching accuracy of the quantum computing framework surpasses traditional approaches by about 35%. These findings demonstrate the significant potential of the quantum computing-based smart healthcare framework for processing large-scale health data in the context of higher-order correlation mining, paving new pathways for the development of smart healthcare. This study utilizes multiple public datasets to achieve breakthroughs in computational speed, higher-order correlation mining, early disease prediction, and personalized treatment plan matching, thus opening new avenues for advancing smart healthcare.