Comprehensive Quantum Computing Applications for Finance Professionals
- Analytics & Tech
This module is conducted in-person.
10 days
Weekday (9am - 5pm)
Who Should Attend
Anyone with a good technical background and comfortable in coding solutions such as technology architects, developers, machine learning developers and data scientists.
PREREQUISITES
The prerequisites expected are:
- Good Python coding experience or similar
- Finance knowledge such as investments or loans
- Machine learning experience would also be beneficial
Overview
Quantum computing, though in its early stages of practical application within the financial services sector, holds significant potential to revolutionise operations, enhance risk management, streamline decision-making processes, and elevate customer interactions. With ongoing advancements in quantum algorithms, hardware capabilities, and software frameworks, the anticipated evolution of quantum computing's impact on financial services is set to intensify in the near future.
This comprehensive 10-day course offers participants an in-depth understanding of quantum computing, its distinctions from classical computing, and its current and future applications in the financial sector. Participants will have the opportunity to programme real quantum computers and delve extensively into quantum algorithms, focusing on optimisation, quantum machine learning, and modelling. As quantum computing continues to progress rapidly, with demonstrations of ""quantum advantage"" and ongoing technological advancements, this course will emphasise the true strength of quantum computing, which lies not only in its computational speed but also in its accuracy for specific use cases.
Learning Objectives
At the end of the 10-day course, participants will be able to:
- Explain the fundamentals of quantum computers
- Recognise the advantages and disadvantages of quantum computers in financial services
- Programme quantum computers for optimisation using Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimisation Algorithm (QAOA)
- Perform quantum machine learning using standard frameworks such as Pennylane
- Model problems using Quantum Approximate Algorithm (QAA), Quantum Amplitude Estimation (QAE) and Quantum Monte Carlo (QMC)
- Recommend quantum computers for the correct problem types
- Predict advancements in quantum computing for utility
Assessment
As part of the requirement for funding, there will be an assessment conducted at the end of the course. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Fee Table
Pending Funding Approval
Please note that the programme fees are subject to change without prior notice.
Intake Information
This module is conducted in-person.
| Course | Dates |
|---|---|
| Intake 1 | [To Be Advised] |
*Online registration will close 5 calendar days before the course start date
