Course overview

This course will introduce the new field of quantum machine learning with application in Finance. The objective is to equip the audience with the foundations of the quantum computations and the key quantum machine learning methods. The methods will be set within the standard machine learning workflow and applied on real financial dataset. The quantum algorithms will be run on real quantum computers.

The course consists of eight 2-hour long sessions, each of which introducing a new topic and deepening the knowledge gathered in the previous ones. The delegates are supposed to spend at least one hour of self-study per week to review and practice the techniques covered in the sessions. In addition, there will be a project assigned to delegates to practice the techniques in the real data environment.

Benefits

You will be introduced to concepts of the quantum computations and the key notion of qubit. You will see number of standard operations we can do with one or more qubits including things like quantum teleportation.

You will learn two machine learning methods: the quantum support vector machine, and quantum neural network. We will also review the elements of the machine learning. Subsequently, you will be guided to set the workflow such that the quantum machine learning methods can be included into your standard workflow. Target audience This course is particularly useful to both professionals and researchers working with machine learning techniques, who are keen to extend knowledge to the new quantum tools.

Course prerequisites

Delegates are expected to have basic knowledge of Python (being able to run simple commands preferably using the Jupyter Notebooks), and basic knowledge of Mathematics and Statistics. Any prior knowledge of Quantum Physics is not required.

Registration, payment and cancellation policy

Payment of course fees is required prior to the course start date.

In case a course is cancelled, registered participants will receive the full refund.

Registration closes 7-calendar days prior to the start of the course.

A 15% discount is available for groups of three or more participants.

Please email [email protected] before purchasing your tickets if you are a group of three or more people. 

Please use your University email address to receive the student rate.