Introduction

As the volume and complexity of financial data continue to grow, traditional machine learning models are reaching their limits in solving complex credit and risk management challenges. Hybrid quantum-classical neural networks, a revolutionary approach combining the power of quantum computing with classical algorithms, are emerging as a game-changer. These networks hold the promise of processing data at an unprecedented speed, identifying subtle patterns, and making highly accurate predictions for tasks like credit scoring, fraud detection, and portfolio optimization. This Quantum Leap: Training Course on Hybrid Quantum-Classical Neural Networks is a 10-day comprehensive program designed to bridge the gap between financial expertise and this transformative technology.

This course will guide you from the fundamentals of quantum mechanics to the practical application of quantum machine learning in finance. You will learn how to design, train, and deploy hybrid models that can outperform traditional methods in a variety of risk scenarios. By the end of this training, you will not only understand the theoretical underpinnings of quantum computing but also be equipped with the hands-on skills to leverage this technology to gain a competitive edge in credit and risk management.

Duration: 10 Days

Target Audience

• Quantitative analysts
• Data scientists in finance
• Risk modelers and managers
• Financial engineers
• Machine learning engineers
• Research and development professionals
• Portfolio managers
• FinTech innovators
• Academics and students in finance
• Technology leaders in financial institutions

Objectives

• Understand the foundational principles of quantum computing.
• Grasp the concepts of qubits, superposition, and entanglement.
• Identify the key differences between classical and quantum algorithms.
• Learn to build and train basic quantum circuits.
• Explore the architecture of hybrid quantum-classical neural networks.
• Apply hybrid models to solve complex credit scoring problems.
• Develop a predictive model for financial fraud detection.
• Evaluate the performance and limitations of quantum models.
• Understand the current landscape of quantum hardware and software.
• Formulate a strategy for adopting quantum technologies in finance.

Course Modules

Module 1: Fundamentals of Quantum Computing

• An introduction to the quantum world
• What is a qubit and how is it different from a classical bit?
• The concepts of superposition and entanglement
• Quantum gates and quantum circuits
• Measuring and reading out quantum states

Module 2: Quantum Machine Learning Primer

• The role of quantum mechanics in machine learning
• Key quantum algorithms for data processing
• The advantages of quantum parallelism
• A comparison of classical and quantum machine learning
• The potential of quantum computing for financial tasks

Module 3: Introduction to Neural Networks

• The architecture of classical neural networks
• The process of forward and backward propagation
• Key concepts: activation functions and loss functions
• Training neural networks with gradient descent
• Practical applications in finance

Module 4: Building Hybrid Quantum-Classical Networks

• The concept of a variational quantum circuit
• Connecting a quantum circuit to a classical neural network
• The role of parameterized quantum circuits
• Designing a hybrid architecture for a specific problem
• The training loop for a hybrid model

Module 5: Quantum Algorithms for Financial Data

• Quantum Amplitude Amplification (QAA) for search
• Quantum Phase Estimation (QPE) for risk analysis
• Grover's algorithm for portfolio optimization
• The use of quantum kernels for classification
• Applying quantum algorithms to financial datasets

Module 6: Implementing Hybrid Models with Quantum Frameworks

• An introduction to PennyLane and Qiskit
• Writing basic quantum circuits in Python
• Building a simple hybrid model with a framework
• Simulating the hybrid network on a classical computer
• Accessing real quantum hardware via the cloud

Module 7: Credit Scoring with Hybrid Networks

• The limitations of traditional credit scoring models
• The use of quantum features to enhance credit risk
• Designing a hybrid model for credit application analysis
• Training the model on a financial dataset
• Evaluating model performance and accuracy

Module 8: Fraud Detection and Anomaly Detection

• The challenges of detecting financial fraud
• The role of quantum networks in identifying complex patterns
• Developing a hybrid model for transaction fraud
• Using quantum circuits for anomaly detection
• Real-world case studies in fraud detection

Module 9: Portfolio Optimization and Risk Management

• The problem of portfolio optimization
• Using quantum annealing and variational algorithms
• Applying hybrid models to solve optimization problems
• Managing market risk with quantum models
• The future of algorithmic trading

Module 10: The Quantum Hardware Landscape

• A review of different quantum computing architectures
• Superconducting qubits vs. ion traps
• The challenge of quantum decoherence and noise
• The concept of quantum volume
• The roadmap for fault-tolerant quantum computers

Module 11: Capstone Project Part 1: Design

• Defining a real-world credit or risk problem
• Designing a hybrid quantum-classical solution
• Outlining the quantum circuit and classical network
• Selecting the right dataset and features
• Planning the experimental setup

Module 12: Capstone Project Part 2: Build

• Writing the full hybrid model in a quantum framework
• Running the model on a quantum simulator
• Parameter tuning and optimization
• Testing the model with different datasets
• Preparing a clear and concise code base

Module 13: Capstone Project Part 3: Present & Discuss

• Presenting the final project and its results
• Explaining the model's performance metrics
• Discussing the challenges and future improvements
• Q&A and peer feedback session
• Receiving expert recommendations and insights

Module 14: Value Proposition & Future Outlook

• The potential ROI of quantum computing in finance
• Identifying new business opportunities
• The skills needed for a career in quantum finance
• The timeline for widespread adoption
• The ethical and societal implications of the technology

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.