The fusion of quantum computing and machine learning holds the potential to revolutionize how we solve complex problems. Quantum computing, with its ability to process vast amounts of data through the principles of quantum mechanics, could accelerate machine learning algorithms, enabling faster and more efficient pattern recognition, optimization, and decision-making. This convergence helps overcome limitations faced by classical computing in fields like artificial intelligence, drug discovery, cryptography, and more. As ...
Read More
The fusion of quantum computing and machine learning holds the potential to revolutionize how we solve complex problems. Quantum computing, with its ability to process vast amounts of data through the principles of quantum mechanics, could accelerate machine learning algorithms, enabling faster and more efficient pattern recognition, optimization, and decision-making. This convergence helps overcome limitations faced by classical computing in fields like artificial intelligence, drug discovery, cryptography, and more. As researchers continue to explore this fusion, the potential applications of quantum-enhanced machine learning increase, opening new possibilities for innovation and problem-solving across industries. Exploring the Fusion of Quantum Computing and Machine Learning explores the revolutionary fusion of quantum computing and machine learning. It examines practical applications, demonstrating how the integration of quantum computing and machine learning algorithms can reveal new solutions for complex problems, paving the way for advancements in various fields. This book covers topics such as neural networks, online marketing, and quantum systems, and is a useful resource for computer engineers, energy scientists, marketers, business owners, medical professionals, academicians, and researchers.
Read Less
Add this copy of Exploring the Fusion of Quantum Computing and Machine to cart. $251.63, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Engineering Science Reference.
