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Quantum computing has shown a potential to tackle specific types of problems, especially those involving a daunting number of variables, at an exponentially faster rate compared to classical computers. This volume focuses on quantum variants of Machine Learning algorithms, such as quantum neural networks, quantum reinforcement learning, quantum principal component analysis, quantum support vectors, quantum Boltzmann machines, and many more.
Quantum computing (QC) can process calculations tenfold quicker than traditional computing by utilizing the unique characteristics of quantum bits or qubits. The three fundamental ideas of quantum mechanics – superposition, entanglement, and interference – give QC its particular strength. A qubit can store quantum information in a state of superposition, which combines all of the qubit’s potential configurations. Computational spaces can be made complex and multidimensional by using multiple qubits in superposition. These spaces allow for many representations of complex problems.
Preface
Quantum computing: a paradigm shift from conventional computing
An exploration of quantum computing: concept, architecture, and innovative applications
Quantum machine learning in healthcare: diagnostics and drug discovery
Quantum machine learning in finance
Crucial role of blockchain in quantum computing: enhancing security and trust
Algorithmic exploration of unveiling fault tolerance in quantum machine learning
Quantum machine learning in renewable energy systems
Decentralized quantum machine learning: distributed quantum computing for enhanced learning
Quantum reinforcement learning: decision-making in quantum environments
Quantum machine learning in natural language processing: opportunities and challenges
Unveiling intelligence: exploring variational quantum circuits as machine learning models
Methods and tools to improve quantum software quality: a survey
Quantum-enhanced neural networks: bridging the quantum algorithm and machine learning
Future trends and research horizons in quantum machine learning