QSC 5th Quantum Training on quantum machine learning in Leiden.

       

Vedran Dunjko (LIACS, Leiden)        Evert van Nieuwenburg (LIACS, Leiden)

 

Quantum meets machine learning: both sides of the coin

 

Day 1: Machine learning for quantum systems
Lecturer: Evert van Nieuwenburg
Abstract: Machine learning can help advance quantum technology, for example through the design of better quantum circuits, the optimization of pulses that control qubits or even through AI-based solutions to quantum error correction. In this lecture series we will cover some basic machine learning techniques and their application to problems in quantum technology, with a focus on hands-on experience with simple examples.

Day 2: Quantum systems for machine learning
Lecturer: Vedran (the Soft-nosed) Dunjko
Abstract: While machine learning (ML) ideas may help build us better quantum computers, the converse is also true: quantum computers allow us to execute certain machine learning algorithms more efficiently and also give us new genuinely quantum ML models which can have provable advantages over all classical schemes.
In this series of lectures, we will give an overview of modern approaches to quantum-enhanced machine learning including: basics of machine learning and statistical learning theory; quantum speed-ups for conventional methods; definitions and basic properties of genuinely-quantum ML models; and some recent results on provable advantages of quantum learners.
While most of the lectures will discuss theory, the last block we will provide a hands-on demo of how quantum learners can be programmed and run on simulators.

 

All quantum postdocs and PhD-students are welcome to attend this training. If you register, we expect you to attend the training as we order drinks and food for you (> 50 euros). For master students (2nd year) there are requirements:

Day 1

- Experience with python (notebooks).
Day 2
- Familiarity with quantum computing and quantum algorithms.
- Basics of coding in python are beneficial due to the last programming block.
- Familiarity with machine learning will be beneficial, although the lectures will not assume prior knowledge.

Please send a short motivation by email to doutzen at cwi dot nl before June 13 (subject line: motivation QSC Quantum Training). 

 

Thursday June 20 by Evert van Nieuwenburg

09.30 Start - registration
10.00 Lecture on use cases: reinforcement learning, bayesian estimation, gradient-based optimization, CMA-ES
11.30 Coffee break
11.45 Hands-on session 1: gradient-based optimization 

13.00 Lunch
14.00 Hands-on session 2: reinforcement learning

15.30 Tea break
15.45 Hands-on session 3: CMA-ES

17.00 End of the day

 

Friday June 21 by Vedran Dunjko

09.30 Start - registration
10.00 Foundations of machine learning and basics of quantum machine learning
11.30 Coffee break
11.45 Quantum classification, generative modelling and reinforcement learning

13.00 Lunch
14.00 Tutorials

15.30 Tea break
15.45 Quantum learning advantages and disadvantages
17.00 End of the day