Quantum computing in part refers to the utilization of qubits which can exist in superposition of both 0s and 1s as opposed to classical bits which can only be represented as 0s or 1s for a single place taken. The very characteristic feature of quantum computing is its ability to emulate properties of quantum mechanisms, like entanglement and superposition. Superposition makes it possible for qubits to exist in many states at once, it means that we can only measure either the 0 basis state or the 1, which are equivalent to binary bits in classical systems. Neural networks, on the other hand, require training across large amounts of data to adjust their hyperparameters like hidden layers, learning rate, nodes, etc., whichever combination of these hyperparameters gives us the least loss is considered to be optimum. The intent of this paper is to explain quantum phenomenon and how we can tap into the probabilistic nature of quantum fluctuations of qubits to find out if it can propose faster ways to reach optimum hyperparameters for a neural network as opposed to traditional methods like Random Search CV or Grid Search CV which are computationally more intensive. The result of this study will be a comparison to check and see whether quantum optimized hyperparameters achieve a faster loss rate as compared to traditional Random Search CV optimization.