Organised by TÜBİTAK ULAKBİM and Middle East Technical University
Please click here to watch the video recordings of the workshop sessions
Date:
23-25 January 2024 (3 sessions)
Event Type: Workshop
Format: Online (Zoom)
Topic: Introduction to Quantum Machine Learning
Overview:
Introduction to Quantum Machine Learning Workshop is a 3-half-day online event (3 sessions in total) on the fundamentals of machine learning frameworks and quantum machine learning. The scope of the workshop covers the preliminaries of generative AI, quantum machine learning, and quantum neural networks.
Online sessions will be held via Zoom.
Agenda:
Please see the Timetable for detailed information. Times given in İstanbul, Türkiye (GMT+3:00)
Day 1 (23 January 2024):
17:00 - 18:00
- Introduction to GAN and AE
- Q&A
Day 2 (24 January 2024):
09:30 - 11:00
- Quantum Machine learning vs Traditional machine learning, energy and data efficiency
- Qiskit Machine Learning Library
- Quantum Neural Networks (QNN)
- QNN regressor and clasifier
- Q&A
Day 3 (25 January 2024):
09:30-11:00
- Barren plateaus and a method to alleviate the problem.
- Quantum Auto Encoder
- Quantum Generative Adversarial network
- Error mitigation in QC with HPCs using mitiq (if time permits)
- Q&A
Language: English
Duration: 3 Half-Day, 3 Sessions
Target Audience: Academia (possible BSc., MSc. and Ph.D. Students) and industry.
Prerequisite(s):
- Familiarity with Python programming language
- Familiarity with PyTorch Workflow Fundamentals (i.e. https://www.learnpytorch.io/01_pytorch_workflow/)
- Familiarity with Qiskit 0.45
- Basic familiarity with Jupyter Notebooks.
- Knowledge of basics of programming (variables and basic data types, loops, and conditionals).
- Preferably being an undergraduate or graduate level student (no restrictions on programs)
- Performing necessary installations (Anaconda, Qiskit etc.)
- A PC with a decent CPU and >16 Gb of ram, with list of python packages in "Python packages required" section installed (preferably in a Conda environment), or an online provider that provides these packages (at simulator level) such as IBM Quantum Platform account (https://quantum.ibm.com/credits-program) Please notice that libraries such as SciPy and NumPy get installed alongside these packages automatically. If some of the required packages do not get installed in your system for whatever reason, you might need to install them in the class session.
Tools, libraries, and frameworks used: Python, Jupyter Notebook, Anaconda/Miniconda, PyTorch, Qiskit
qiskit 0.44.3
qiskit-aer 0.13.0
qiskit-algorithms 0.2.1
qiskit-machine-learning 0.7.0
qiskit-nature 0.7.0
qiskit-qulacs 0.0.1
qiskit-terra 0.25.3
torch 2.1.0
torchvision 0.16.0
jupyter 1.0.0
mitiq 0.30.0
matplotlib 3.7.3
pylatexenc 2.10
pandas 2.0.3
Learning Objectives: By participating in this course, you will have information about machine learning methods and an introduction to hybrid quantum machine learning models where HPCs and Quantum computers work together.
About the instructors:
Dr. Sinan Kalkan (Computer Engineering, METU) received his BSc. and MSc. degrees from Middle East University, Computer Engineering. He completed his doctorate at the University of Göttingen, Germany. After working as a postdoctoral researcher at the University of Göttingen and METU for a while, he joined METU Computer Engineering Department as a faculty member. His main research focuses on computer vision and machine learning.
Dr. Barış Malcıoğlu (Physics, METU) received his BSc, MSc. and PhD degrees from METU Physics department. He worked as the developer of the theoretical spectroscopy module from basic principles at Quantum Espresso at SISSA. After working at the Physics department of the University of Liege, the Materials sciences department of Erlangen FAU University, and the physics department of the University of Salzburg, he started working as a faculty member at METU Physics department in 2019. Dr. Malcıoğlu teaches undergraduate courses on Quantum Computing for Scientists and Machine Learning with Quantum Computers. His main research areas are quantum artificial neural networks, computational spectroscopy and functionalized surfaces.
Contact: ncc@ulakbim.gov.tr
Acknowledgments
This event was supported by the EuroCC 2 project. This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Türkiye, Republic of North Macedonia, Iceland, Montenegro, Serbia.
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