Coming soon - Quantum Machine Learning (QML) by Jack Jacquier 
About the programme:
Quantum computing has been developing independently of machine learning for quite some time. Quantum computing promises significant performance advantages over traditional computing methods. For example, it may be possible to use quantum computing to break complex cryptographic schemes, thus rendering many ciphers easily crackable. While industrial grade quantum computers remain somewhat limited due to the relatively low number of qubits and noise-related technical difficulties, the field is advancing rapidly.
Led by Quantum Computing subject matter expert Jack Jacquier
Duration:
📅 Self-Paced: 8 lecture hours
Format:
💻 Online
📆 Time Commitment:
Recorded lectures accessible any time.
🕛 Self-paced Online:
Students will have the opportunity to apply what they learn in hands-on projects throughout the course.
💳 Cost: £345.00
Course Modules & Outline
Module 1: Introduction to Quantum Computing
Principles of Quantum Mechanics
- Postulate 1 – Statics
- Postulate 2 – Dynamics
- Postulate 3 – Measurement
- Postulate 4 – Composite systems
Module 2: Variational Circuits as Machine Learning Methods
Quantum Neural Networks
- From classical to quantum
- Data encoding
- Training QNN
Quantum Circuit Born Machine
- QCBM
- Kernels
- QCBM vs RCBM
- Quantum reservoir Computing for error bounds
Module 3: Quantum Models as Kernel Methods
- Optimisation from a quantum perspective
- Variational Quantum Eigensolver
- Quantum Approximate Optimisation Algorithm
Module 4: Potential Quantum Advantages
Q annealing
- Simulated annealing
- Quantum annealing
- Adding noise.....
- Example in Finance
Monte Carlo
- Classical Monte Carlo
- Quantum Monte Carlo
- Quantum simulation
Solving PDEs
Final Assessment: Online Test
-
Multiple-choice and short-answer questions assessing conceptual understanding
Learning Outcomes:
This course provides a comprehensive introduction to quantum computing, exploring its principles and applications in machine learning and optimization. Beginning with the foundational postulates of quantum mechanics, it establishes the theoretical framework necessary to understand quantum systems. The course then delves into variational circuits as machine learning methods, covering quantum neural networks, data encoding, and training techniques. It further explores quantum models as kernel methods, including optimization techniques such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm. Finally, the course examines potential quantum advantages, such as quantum annealing and Monte Carlo methods, with practical applications in finance and simulation.
Jack Jacquier – Professor, Imperial College London & Research Fellow, The Alan Turing Institute
Antoine (Jack) Jacquier is a Professor of Mathematics at Imperial College London, where is is co-heading the MSc programme in Mathematics and Finance. His research focuses on stochastic analysis and volatility modelling as well as quantum computing with applications to Finance. He also serves as a scientific consultant and advisor for various Finance and Technology companies.