Quantum Applications to Finance

This series of tutorials explores the intersection of quantum computing and finance through hands-on notebooks using CUDA-Q. Financial institutions face complex computational challenges in forecasting and optimization, and recent advancements in quantum computing are being explored by academia and industry as potential solutions to these traditionally difficult problems (Herman et al). By implementing quantum algorithms with CUDA-Q, you will gain insights into financial modeling challenges and explore hybrid quantum-classical algorithms that can be applied to a wide range of other fields.

This series is designed to be modular. Notebooks 1 and 2 are a two-part series that should be completed in sequence. All other notebooks are fully independent and can be explored in any order. Feel free to dive into whichever topic interests you most!

📚 What You'll Learn In Notebooks 1 & 2, you'll dive into the fundamentals of quantum walks, see how they differ from classical random walks, and apply them to model financial data.

Notebook 3 demonstrates how quantum computing can optimize investment portfolios. You'll explore three different approaches: the Quantum Approximate Optimization Algorithm (QAOA), quantum annealing, and a novel algorithm by Infleqtion called QChop.

Pre-requisites: Learners should have familiarity with Jupyter notebooks and programming in Python and CUDA-Q. It is assumed the reader has some familiarity already with quantum computation and is comfortable with braket notation and the concepts of qubits, quantum circuits, measurement, and circuit sampling. The CUDA-Q Academic course entitled "Quick Start to Quantum Computing with CUDA-Q" provide a walkthrough of this prerequisite knowledge if the reader is new to quantum computing and CUDA-Q or needs refreshing.

Notebooks

The Jupyter notebooks in this folder are designed to run in an environment with CUDA-Q with Python. For instructions on how to install CUDA-Q on your machine, check out this guide. A Dockerfile and requirements.txt are also included in the main directory of the repository to help get you set up.

Otherwise, if you have set up an account in any of the platforms listed below, simply click on the icons below to run the notebooks on the listed platform.

Notebook	qBraid[^1]	CoCalc[^2]	Google Colab[^3]
Quantum Walks for Finance Part 1		Open in CoCalc
Quantum Walks for Finance Part 2		Open in CoCalc
Portfolio Optimization		Open in CoCalc
[^1]:If using qBraid Lab, use the Environment Manager to install the CUDA-Q environment and then activate it in your notebook. In qBraid Lab you can switch to a GPU instance using the Compute Manager.
[^2]:After following the link, select the "Edit your own copy" button, and either select or create a project. Use the run icon in the upper toolbar to execute Python cells.
[^3]: You will need to run the command !pip install cudaq in a python code block in each notebook to run on Google CoLab.