Overview

Bridge quantum computing and quantitative finance. Master algorithms for portfolio optimisation, risk modelling, and derivative pricing using quantum circuits and hybrid quantum-classical systems.

Module 1: Quantum Computing Foundations for Finance

Qubits, quantum states, and Bloch sphere representation

Superposition and entanglement with financial interpretation limits

Quantum gates and basic circuit operations (Hadamard, Pauli, CNOT)

Measurement and probabilistic outcome modelling

Noise, decoherence, and NISQ-era constraints

Relevance of quantum computing in financial computation

Module 2: Quantum Linear Algebra & Fourier Methods in Asset Pricing

Vector spaces, tensor products, and quantum state representation

Quantum Fourier Transform (QFT) fundamentals

Phase estimation and amplitude estimation techniques

Quantum approaches to numerical integration problems in finance

Applications in option pricing models (theoretical and hybrid use cases)

Computational advantage vs classical Monte Carlo methods

Module 3: Quantum Monte Carlo & Derivative Pricing

Classical Monte Carlo methods for pricing and risk modelling

Quantum amplitude estimation for variance reduction

Quantum-enhanced simulation of stochastic processes

Value at Risk (VaR) and Expected Shortfall estimation

Error convergence analysis and complexity comparison

Limitations of current quantum hardware in financial simulation

Module 4: Portfolio Optimisation with Quantum Algorithms

Mean-variance portfolio theory fundamentals

Quadratic Unconstrained Binary Optimisation (QUBO) formulation

Quantum Approximate Optimisation Algorithm (QAOA)

Quantum annealing approaches for optimisation problems

Constraint handling: risk limits, diversification, and costs

Benchmarking quantum solutions against classical optimisation methods

Module 5: Quantum Machine Learning for Financial Systems

Quantum kernel methods for high-dimensional data mapping

Pattern recognition in financial time series data

Fraud detection using quantum-enhanced classification

Hybrid quantum-classical machine learning models

Feature encoding strategies for financial datasets

Model evaluation and performance metrics in financial ML

Module 6: Hybrid Quantum Finance Systems & Applied Case Studies

Hybrid workflows using Qiskit and PennyLane with classical systems

System architecture: preprocessing, quantum layers, and post-processing

High-frequency trading simulation frameworks

Climate risk analytics using stochastic quantum models

End-to-end design of quantum-finance pipelines

Practical limitations, scalability challenges, and future outlook

Tools & platforms

Capstone project

Build a quantum-enhanced asset allocation model and benchmark against classical methods.

Starting Date: 4th July 2026
Course Fee: £3990

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