This article investigates the intersection of quantum computing and artificial intelligence (AI) in the financial sector, analyzing their impact on decision-making, investment portfolio optimization, and market forecasting. Through a mixed-methods approach combining expert interviews and a comprehensive literature review, the opportunities and challenges presented by these emerging technologies are identified. The results show that AI is revolutionizing the efficiency and accuracy of data analysis, while quantum computing promises to solve complex optimization problems that are difficult for classical computers. However, the adoption of these technologies faces ethical and regulatory challenges, such as the need for transparency in automated decisions and the protection of sensitive data. It is concluded that staff training and collaboration between institutions, academics, and regulators are essential for effective and responsible implementation.
With the development of intelligent algorithms, new challenges have emerged in the financial field in optimizing the investment portfolio. In view of the current problems existing in portfolio optimization, the particle swarm algorithm (Particle Swarm Optimization, PSO) and quantum approximation optimization algorithm (Quantum Approximate Optimization Algorithm, QAOA) of the initial population Qanta coding is improved, and a hybrid algorithm suitable for solving enterprise portfolio optimization problems is proposed. The results found that the average performance of this method is below 0.06, which is better compared with the comparison algorithm. At the same time, with the increase of the risk level, the maximum, minimum and average increase of the yield are
10.99%,19.80% and 7.98% respectively, stabilizing. The results show that the research algorithm is effective in solving the enterprise portfolio optimization problem, has better optimization ability in practical application, and can provide reference for enterprise investors to make investment decisions.
Quantum-driven brings the new innovation in the financial sectors and “predictive analytics, portfolio optimization, and autonomous” trading systems. Quantum computing's is the complex, high-dimensional data sets at unprecedented speed, financial institutions envision more accurate forecasting models and decision-making. It is the one of the foremost barriers is the immaturity of current quantum hardware, instability, and scalability limitations. On the other hand integration of quantum systems with existing classical infrastructures require “hybrid architectures, specialized algorithms, and substantial investment” in infrastructure and training. Quantum-enhanced models create opaque decision-making processes, amplifying risks of market exploitation, data privacy violations, or systemic vulnerabilities. Collaborative ecosystems involving academia, industry, and regulators are expected to accelerate the development of standards and best practices.