Quantum Computing for Finance

About the course

Quantum Computing for Finance is an advanced course in the emerging field of quantum computing for finance. This technical course develops an understanding of the quantum algorithms behind financial applications and how to implement them on quantum computers. You will build the core algorithmic toolkit (Fourier transforms, phase estimation, amplitude amplification and estimation), then apply it to expectation values, combinatorial optimisation and portfolio rebalancing, and finally to quantum option pricing. Each module includes hands-on notebooks.

Instructor

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Learning objectives

By the end of the course, you will be able to: 

• Construct and simplify the Quantum Fourier Transform (QFT) circuit and use it for quantum arithmetic. 
• Apply phase estimation, amplitude amplification and amplitude estimation, and analyse their complexity. 
• Use quantum amplitude estimation to compute expectation values, including how functions and distributions are encoded in quantum circuits. 
• Formulate combinatorial optimisation problems (e.g. MAXCUT) for quantum solvers using the quantum adiabatic algorithm and QAOA. 
• Apply QAOA to portfolio rebalancing. 
• Implement and simulate a simple quantum option pricing algorithm using fixed-point quantum arithmetic and Matrix Product States.
• Assess where quantum-enhanced methods offer real opportunities in financial use cases. 

What skills you will learn

• Technical Skills: Building QFT, phase estimation, Grover-type search and amplitude estimation circuits. Preparing quantum states with the Grover–Rudolph algorithm. Encoding arithmetic functions in circuits. Solving optimisation problems with the adiabatic algorithm and QAOA. Simulating quantum circuits with Matrix Product States. 
• Business Skills: Identifying which financial problems (pricing, risk, optimisation) are candidates for quantum acceleration. Judging the maturity and limits of current approaches, and making informed decisions about quantum investment and R&D in financial services. 

Course Structure

Course Prerequisites

Working knowledge of Python, linear algebra, and the basics of quantum computing. Familiarity with basic finance concepts is helpful but not required. 

Duration

The estimated duration to complete this course is approximately 22 hours.

Assessment

Non-graded knowledge checks at the end of each lesson.

Pass final quiz by scoring 80% or more to complete the course and earn the certificate upon completion.

Who Should Enrol

This course is ideal for Software Developers, Data Scientists, Engineers, Researchers, and Students. It is also well suited to quantitative analysts, financial engineers and risk professionals who want to understand quantum algorithms for finance. 

Why Enrol

Enrol to gain a rigorous, hands-on understanding of how quantum algorithms could transform computationally intensive tasks in finance, such as derivative pricing, risk estimation and portfolio optimisation. You will move from the core building blocks to working implementations, and finish with the knowledge to separate genuine near-term opportunities from hype. This course positions you at the intersection of quantum computing and quantitative finance, two fast-moving fields where demand for skilled practitioners is growing.