Quantum Device Solving QUBO for Portfolio Optimization

A machine learning model is introduced as an intermediary between the quantum device and the decision-making process. The quantum device solves QUBO problems to generate training data, the machine learning model learns from this data, and then provides recommendations. This intermediary approach allows the system to leverage quantum computing power while managing complexity through a more interpretable machine learning layer.

The system is divided into distinct modules: a quantum computing module for solving QUBO optimization problems, a machine learning module for learning from quantum-generated solutions, and a decision support module for providing recommendations. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while maintaining high productivity.

The system performs preliminary actions by using the quantum device to solve QUBO problems on historical data before actual decision-making is needed. This generates optimal solutions in advance that are used to train the machine learning model, allowing the model to be prepared and adapt to future periods without requiring complete historical data to be available at the time of prediction.

The machine learning model serves itself by learning patterns from quantum-generated optimal solutions. Once trained on the quantum-generated training set, the model can independently provide recommendations for future periods without requiring additional quantum computing resources or complete historical data, making the system self-sufficient after initial training.