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.
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.
Otherwise, explore our Learning Pathways page for additional cloud-based options to run these notebooks.