Quantum Computing and Machine Learning: In Future to Dominate Classical Machine Learning Methods with Enhanced Feature Space for Better Accuracy on Results

Abstract

AbstractQuantum Computing is new standard which will contribute computational efficiency on to the many operational methods of classical computing. Quantum computing motivates to use of quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. The quantum computing concept need to understand Qubit which is nothing but Quantum Bit that differs quantum computing from classical computing. Classical bit, which can be either Zero 0 or One 1 in single state at a time moment, a Qubit or Quantum Bit can be Zero 0 and One 1 at same time called as in superposition state. Quantum Computers will use quantum superposition and quantum entanglement are the two basic laws of quantum physics principles. Computational tasks which are non-computable by classical machine can be solved by quantum computer and these computational tasks defines heavy computations those expects large size data processing. Machine learning on classical space is very well set but it has more computational requirements based on complex and high-volume data processing. This paper surveys and propose model with integration of quantum computation and machine learning which will make sense on quantum machine learning concept. Quantum machine learning helps to enhance the various classical binary machine learning methods for better analysis and prediction of big data and information processing.KeywordsQubitQMLSupervised MLBloch SphereSuperpositionEntanglement