Inspiration

Stock market simulations often rely on classical stochastic models, such as Monte Carlo methods, to predict financial trends. However, these methods are computationally expensive and struggle to capture the full range of possible market states efficiently. Inspired by advancements in quantum computing, we saw an opportunity to leverage quantum superposition to simulate multiple stochastic states simultaneously. *By integrating quantum algorithms into Brownian motion simulations, our goal was to create a more efficient and accurate model for representing stock behavior. *

What it does

QuStoch utilizes quantum computing principles to enhance financial market simulations. Instead of sequentially generating possible stock price movements using classical Monte Carlo methods, our system employs a quantum algorithm to represent multiple stochastic states in parallel using superposition. By encoding Brownian motion paths into qubits, we achieve a more comprehensive and probabilistically accurate representation of stock price fluctuations. The simulation results are then processed and visualized in a web application, allowing users to analyze stock behavior more effectively.

How we built it

We built QuStoch using a combination of quantum and classical computing frameworks.

Quantum Component: We used quantum circuits that encodes stochastic paths into qubits, leveraging Hadamard and controlled rotation gates to represent different possible stock price movements. The final probability distribution is measured to extract simulated price trajectories. Classical Processing: A Python-based backend processes the quantum-generated data, performing additional statistical analysis and calibrating the results to historical stock trends. Web Interface: Flask and HTML/CSS/JavaScript were used to create an interactive dashboard where users can input initial stock parameters and view real-time simulation outputs. MatPlotLib was integrated for dynamic graph visualizations of stock behavior over time.

Challenges we ran into

One of the biggest challenges was designing a quantum algorithm that efficiently simulates Brownian motion while remaining computationally feasible on current quantum hardware. Mapping stochastic paths onto qubits required careful formulation of probability amplitudes. Additionally, noise and decoherence in quantum circuits affected accuracy, requiring error-mitigation techniques. Finally, integrating quantum-generated data with classical analysis and presenting meaningful financial insights in a web application was a complex task.

Accomplishments that we're proud of

We successfully implemented a working prototype that demonstrates the potential of quantum computing for financial modeling. Achieving a parallelized approach to stochastic simulations and integrating it with a real-time visualization tool were significant milestones. We’re also proud of optimizing our quantum circuit to run on current NISQ (Noisy Intermediate-Scale Quantum) devices while maintaining meaningful results.

What we learned

This project deepened our understanding of quantum computing’s applications in finance, particularly in stochastic modeling. We gained valuable experience in designing quantum circuits for probabilistic simulations and integrating quantum algorithms with classical computation. Additionally, we learned about the limitations of current quantum hardware and the importance of hybrid quantum-classical approaches for real-world applications.

What's next for QuStoch

Moving forward, we aim to refine our quantum algorithm to improve accuracy and scalability. Enhancements include incorporating quantum error correction techniques and exploring alternative quantum algorithms for financial modeling, such as quantum walks. We also plan to extend the web application with more advanced analytics, historical data integration, and real-time stock tracking. As quantum hardware improves, QuStoch has the potential to revolutionize financial forecasting by offering unprecedented efficiency in market simulations.