Henkle, EvanIgur, VismayKarnik, SaraVelaga, SourabhQuantum computers are becoming more and more applicable to a variety of applications due to their ability to exponentially speed up computation. This project aims to utilize quantum technology to build a Quantum Approximation Optimization Algorithm (QAOA), to redefine portfolio optimization in finance. We aim to conduct a series of simulations to evaluate the effectiveness of our quantum based portfolio strategy, and compare the outcomes against those achieved through traditional optimization methods. Our preliminary research indicates that the quantum approach may result in faster and higher quality portfolio solutions, leading to more profitable and risk averse investments. By translating the challenge of finding the optimal combination of assets - balancing risk and return - into a problem that can be solved by quantum computing, we unlock new possibilities for financial analysis and decision making.enFirst-Year Innovation and Research Experience (FIRE)physicsasset managementportfolio optimizationqmlquantum machine learningquantum computingOther