Bapanapalli, AbhinavMahmud, AteefNadig, ChiraagThakker, KrishPredicting stock price movements is a complex challenge faced by many traders and analysts. Our research leverages Quantum Gramian Angular Field (QGAF) transformations combined with Convolutional Neural Networks (CNNs) to classify stock price trends as "up" or "down." By transforming 1D time-series stock data into 2D images, we enable CNNs to extract features more effectively, showcasing the potential of quantum machine learning in financial forecasting.en-USThe First-Year Innovation & Research Experience (FIRE)FIRE Quantum Machine LearningQuantum CircuitsMachine LearningFinancial ForecastingTime-Series AnalysisOther