The intersection between machine learning and quantum computing has been dubbed quantum machine learning, and has attracted considerable attention in re- cent years .This has led to a number of recently pro- posed quantum algorithms
Supervised learning with quantum enhanced feature spaces
Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pat- tern recognition, with many well-known method for classification problems. However, there are limitations to the successful solution to such problems when the feature space becomes large, and the kernel functions become computationally ex- pensive to estimate. A core element to computational speed-ups afforded by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference.
Quantum algorithms for learning
Development of quantum algorithms for quantum generalizations of classical learning models. It can provide possible speed-ups or other improvements in the deep learning training process. The contribution of quantum computing to classical machine learning can be achieved by quickly presenting the optimal solution set of the weights of artificial neural networks.
Quantum algorithms for decision problems
Classical decision problems are formulated in terms of decision trees. A method to reach the set of solutions is by creating branches from certain points. However, when each problem is too complex to be solved by constantly dividing it into two, the efficiency of this method decreases. Quantum algorithms based on Hamiltonian time evolution can solve problems represented by a number of decision trees faster than random walks.
Quantum search
Most search algorithms are designed for classical computing. Classical computing outperforms humans in search problems. On the other hand, Lov Grover provided his Grover algorithm and stated that quantum computers can solve this problem even faster than classical computers. AI-powered by quantum computing can be promising for near term applications such as encryption.
Quantum game theory
Classical game theory is a process of modelling that is widely used in AI applications. The extension of this theory to the quantum field is the quantum game theory. It can be a promising tool for overcoming critical problems in quantum communication and the implementation of quantum artificial intelligence.
