28th of April
Atlas Building, 6º floor, Rosenheimer Straße 143 c, Munich
Do IT Now is gathering the Quantum & AI community for an exclusive Quantum Neural Networks (QNN) Workshop, led by the brilliant Lourens van Niekerk – Quantum Specialist & Educator.
Workshop Objectives:
- Understand the fundamental principles of quantum neural networks (QNNs).
- Implement and train simple QNNs using Qiskit.
- Explore current research trends and enterprise challenges in QNNs.
- Evaluate quantum readiness and return on investment (ROI) for organizations.
High-Intensity Learning:
- Master QNN Fundamentals & Variational Circuits: Go beyond the basics.
- LIVE Coding in Qiskit: Compile, run, debug – make qubits work for you!
- Forge Quantum-HPC Hybrid Strategies: Learn practical integration tactics.
- Solve Problems: Apply QNNs to Risk Analysis, Protein Folding & Graph Opti.
- Entangle with Europe’s Quantum Elite: Network, debate, and collaborate.
Who could be interested in beeing there?
- Quantum Researchers
- AI & Data Scientists
- Quantum Engineers &
- Developers
- PhD Students & Postdocs (Quantum/ML)
- HPC & AI Practitioners ready for Quantum integration
Additional Information:
- Wi-Fi: Available throughout the venue
- Materials: Digital copies of slides and Jupyter Notebook examples provided
- Certification: Participants will receive a certificate of completion
- Preparation: Install Qiskit in advance and review quantum circuits & neural network fundamentals
- Cost: FREE
Agenda
Morning Session: Theory & Practice
09:00 – 09:30 | Registration & Welcome
09:30 – 10:00 | Quantum Computing: how did we get here, what is real, and what is ‘here’?
- History of quantum computing till now
- How does the hype compare to reality
- Refresh basic concepts and terminology
10:00 – 10:45 | Quantum Machine Learning (QML) with Qiskit
- Encoding classical data to quantum
- Quantum analogous algorithms e.g. quantum SVC (QSVC)
- Variational Quantum Classifier (VQC)
- How does this compare to classical ML currently
10:45 – 11:15 | Neural Networks: Classical vs Quantum
- How do classical neural networks (NNs) work?
- How do quantum neural networks (QNNs) differ?
- Why and when might quantum be better than classical?
11:15 – 11:30 | Coffee sip Break
11:30 – 12:30 | Overview of various QNNs
- Quantum Autoencoders (QAEs) and hybrid variants
- Quantum Convolutional Neural Networks (QCNNs)
- Quantum Reservoir Computing (QRC)
- Quantum Generative Adversarial Networks (QGANs)
12:30 – 13:00 | Hands-on Quantum Playground with Jupyter Notebooks
- Guided code examples on QML and QNNs
13:00 – 14:00 | Lunch break & networking
Afternoon Session: Applications & Vision
14:00 – 14:30 | Optimization techniques
- Classical optimization algorithms
- Error correction
- Error mitigation
14:30 – 15:00 | Classical and Quantum Hybridization
- Challenges in bringing quantum to industry
- Current software and tools to bridge the challenges
- Success stories of quantum in HPC
15:00 – 15:15 | Commercial expectations for QNNs
- Which industries are exploring the use of QNNs
- What do the experts say?
15:15 – 15:30 | Summary and further
- Key takeaways from presentations
- Follow-up topics of interest for the curious e.g. AI in QC
15:30 – 15:45 | Coffee Break
15:45 – 16:45 | Industry-Specific QNN Applications
- Monitoring: Anomaly detection via Quantum Autoencoders
- Pharmaceuticals: Polymer prediction via Quantum Reservoir Computing
- Supply Chain: Management via Quantum Convolutional Neural Networks
16:45 – 17:15 | Panel Discussion: Overcoming Enterprise QNN Challenges
- Addressing costs, complexity, and adoption barriers
- Open Q&A session with participants
17:15 – 17:30 | Panel Q&A
17:30 | Closing Remarks and End of Workshop
17:30 – 18:30 | Open Networking Reception
