This project deals with the implementation of Bayesian networks, a type of artificial intelligence based on graph theory and conditional probability, in the quantum computing paradigm. Examples related to the world of sports will be used as input in order to test the developed model. Besides, a comparison between implementations in classical and quantum paradigms will be carried out, so their differences in performance and attributes can be appreciated. The work will conclude with a debate about quantum computing and its future as a basis for executing AI models.
This project deals with the implementation of Bayesian networks, a type of artificial intelligence based on graph theory and conditional probability, in the quantum computing paradigm. Examples related to the world of sports will be used as input in order to test the developed model. Besides, a comparison between implementations in classical and quantum paradigms will be carried out, so their differences in performance and attributes can be appreciated. The work will conclude with a debate about quantum computing and its future as a basis for executing AI models.
Quantum-enhanced Bayesian Network Inference addresses the computational challenges associated with large, complex Bayesian networks through the integration of quantum algorithms. This paper introduces a novel technique leveraging Grover's search to accelerate conditional probability computations, ultimately enhancing inference efficiency. The core mechanism centers around quantum-enhanced probability updates, offering a potential paradigm shift in the processing of large network data. We explore the theoretical implications and preliminary experimental results demonstrating the efficacy of this approach, highlighting its potential to overcome limitations of traditional methods. The paper concludes with a discussion of future research directions, focusing on scalability and practical application.
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.
Quantum computing and neural networks have revolutionized computational intelligence and have the potential to bring revolution in a wide range of scientific, industrial, and technological fields. In some computational activities, quantum computing utilizes the concepts of superposition, entanglement, and quantum parallelism and performs better than classical computers by processing complicated data with high speed and efficiency. Neural networks have transformed decision-making, natural language processing, and pattern recognition especially deep learning models. By incorporating quantum computing into neural network architectures, Quantum Neural Networks (QNNs) open new paradigms in data encoding, feature representation, and learning dynamics by overcoming the computational constraints and scaling related problems of classical models. In this chapter, the theoretical basis, latest developments and possible applications of a quantum neural network have been discussed. This chapter covers the constraints of near-term quantum devices, quantum noise, data encoding techniques, and practical difficulties in hardware implementation. Real-world applications in quantum chemistry, financial modeling, cryptography, and biological imaging have been studied. To fully realize the promise of quantum neural processing, this chapter concludes by describing current research shortcomings and providing opportunities for multi-disciplinary collaboration among computer scientists, physicists, and AI researchers.
Quantum-enhanced Bayesian inference offers the potential to significantly accelerate probabilistic predictions compared to classical Bayesian methods, particularly in complex Bayesian networks. This paper explores the implementation of a novel quantum-enhanced Bayesian inference algorithm, utilizing quantum computation to optimize Bayesian updates and inference. We present a framework for leveraging quantum hardware to enhance the accuracy and efficiency of probabilistic predictions within Bayesian network modeling. The core mechanism focuses on quantum-assisted Bayesian updates, designed to overcome computational bottlenecks inherent in traditional Bayesian inference. The proposed algorithm demonstrates improved accuracy and scalability through a rigorous analysis of its performance on a representative set of Bayesian network scenarios. The investigation delves into the potential advantages and challenges of integrating quantum computing into the Bayesian inference process.
The integration of Artificial Intelligence (AI) and Quantum Computing (QC) is poised to revolutionize highperformance engineering systems. This paper explores the synergistic potential of these advanced computational paradigms, highlighting their applications, challenges, and future directions. We begin by providing an overview of AI and QC, followed by a detailed examination of their integration in various engineering domains. Key algorithms and techniques are discussed, and case studies are presented to illustrate the practical benefits and limitations. The paper concludes with a discussion on the ethical and practical implications of this integration, offering recommendations for future research and development