Название: Advances in Quantum Inspired Artificial Intelligence: Techniques and Applications
Автор: Satchidananda Dehuri, Monalisa Jena, Sarat Chandra Nayak, Margarita N. Favorskaya, Smaranda Belciug
Издательство: Springer
Год: 2025
Страниц: 247
Язык: английский
Формат: pdf (true), epub
Размер: 23.8 MB

This book advances the Artificial Intelligence (AI) techniques by strongly inspired from Quantum Computing. Quantum Computing (QC), rooted in the mysterious principles of quantum mechanics, has fascinated scientists, researchers, and visionaries for a long time. Its potential lies in changing how we compute, moving from classical bits to quantum bits (qubits), and unlocking incredible processing capabilities. At the same time, Artificial Intelligence (AI) has been on a trajectory of rapid advancement, with Deep Learning becoming proficient at tasks that were once deemed impossible. At this juncture, integrating these two fields through various means can play a pivotal role to bring a technological revolution.

Bioinspired algorithms are a class of optimization techniques designed by mimicking different aspects of nature such as natural selection, brain-like processing, collective behavior, and cooperation. Bioinspired techniques have successfully addressed complex challenges across diverse fields like Artificial Intelligence, Data Science, optimization, pattern recognition, and cybersecurity. Quantum computing is a seamless integration of the quantum mechanics concepts and Computer Science algorithms. It has the ability to redefine computation by offering exponential speedups for certain problems. This makes quantum computing a transformative tool for advancing bioinspired algorithms, especially in addressing challenges involving high-dimensional search spaces and complex constraints. Genetic algorithm is one of the notable bioinspired technique because of its flexibility and resilience across a wide range of optimization challenges. Drawing inspiration from the principles of natural selection and evolution, GA uses mechanisms such as selection, crossover, and mutation to refine solutions iteratively, adapting them over time to achieve optimal or near-optimal solutions.

Artificial neural network (ANN) is another fascinating bioinspired algorithm into which academicians are interested in incorporating quantum computing principles. Quantum-enhanced neural networks can surpass the constraints of traditional ANNs, particularly in handling large datasets, and boosting the network’s capacity to identify complex patterns. The Chapter 1 offers an exciting exploration of the powerful intersection between quantum computing and bioinspired algorithms, a topic that is becoming increasingly vital in today’s rapidly evolving technological landscape. As quantum computing continues to evolve over time, its integration with bioinspired methods like genetic algorithms and neural networks holds the ability to transform the optimization and machine learning techniques. In this chapter, we aim to cover the fundamentals of quantum computing, bioinspired algorithms, and highlight the influence of quantum technologies on genetic algorithms and neural networks.

Deep Learning has demonstrated remarkable success in various domains, yet the computational demands of training large neural networks continue to pose challenges. The research by Chiranjevi et al. investigates the integration of Quantum Computing principles into neural network architectures, aiming to explore and exploit the potential quantum advantages in deep learning tasks. Their study focuses on Quantum Neural Networks (QNNs), where quantum bits (qubits) are leveraged to encode and process information in quantum superposition. Quantum entanglement and parallelism offer unique possibilities for enhancing the expressiveness and computational efficiency of deep learning models.

The book is crafted for a diverse readership, from seasoned researchers and practitioners in Quantum Computing and AI to students and enthusiasts eager to grasp the essence of this transformative convergence. At last, we extend a very deep sense of gratitude to the authors who have contributed their valuable work to fulfill the goal of the book.






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