Supercomputing leverages quantum machine learning and Grover's algorithm

Bikram Khanal, Javier Orduz, Pablo Rivas, Erich Baker

Abstract: The complexity of searching algorithms in classical computing is a classic problem and a research area. Quantum computers and quantum algorithms can efficiently compute some classically hard problems. In addition, quantum machine learning algorithms could be an important avenue to boost existing and new quantum-based technology, reducing the supercomputing requirements for executing such problems. This paper reviews and explores topics such as variational quantum algorithms, kernel methods, and Grover’s algorithm (GA). GA is a quantum search algorithm that achieves a quadratic speed improvement as a quantum classifier. We exploit GA or amplitude amplification to simulate rudimentary classical logical gates into quantum circuits considering AND, XOR, and OR gates. Our experiments in our review suggest that the algorithms discussed can be implemented and verified with relative ease, suggesting that researchers can investigate problems in the areas discussed related to quantum machine learning and more.