Financial markets known for their everchanging and intricate nature have been a huge topic in stock market prediction research. Traditional methods typically use regular computer-based learning to figure out patterns in past data. The game-changing impact of quantum machine learning on predicting stock markets is significant. Quantum computing with its principles of superposition and entanglement have the ability to handle complex calculations and analyze large datasets at once shows promising results. In this work, a quantum variational circuit is used which is a classifier and is designed to optimize parameters and find the best solution to a problem which in our case is to learn pattern and trends. The circuit was incorporated using different optimizers like “Real Amplitude” and “efficientSU2” to obtain the best possible results. With this we achieveda very high accuracy measured on the basis of quantum variational score when applied to a cryptocurrency-based stock dataset. However, despite the outstanding results it takes more time because of unavailability of widespread access to commercial quantum computers but it certainly holds potential to equally predict as the trivial methods.