New approach of KNN Algorithm in quantum computing based on new design of quantum circuits
Vu Tuan Hai, Phan Hoang Chuong, Pham The Bao
Abstract
Quantum computing has risen as the new type of computer that theoretically reduces the time complexity of classical methods exponentially because of the nature of superposition. It is promising to reduce runtimes on large data sets in machine learning (ML). Meanwhile, k-nearest neighbor (kNN) is a simple ML algorithm that can be translated to a quantum version (QkNN) to perform classification tasks efficiently. Here, we show a new version of QkNN which has a speed up in time complexity by using a new design of quantum circuits called integrated swap test. This quantum circuit can load two N-dimensional states and calculate the fidelity between them. Results show that QkNN allows us to take a different approach to the ML field.