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Lecturer(s)
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Course content
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Week 1: Limits of classical simulations - Comparison of conventional methods and hybrid algorithms for strongly correlated materials (Post-DFT era). Week 2: Digital material model - Specifics of data preparation and conversion of crystal lattices into representations for quantum hardware. Week 3: Calculation of material stability - Demonstration of the use of available libraries (SDK) to find the energy minimum of molecules. Week 4: Engineering simulation strategies - Choice of computational approach (Ansatz) with regard to hardware costs and accuracy. Week 5: Applications in energy - Case studies of material modeling for Li-ion batteries and catalysis. Week 6: Combinatorial optimization - Introduction to the formulation of material tasks for optimization algorithms (e.g., QAOA). Week 7: Optimization of manufacturing processes - Practical examples of finding the optimal composition of batches or alloy arrangements. Week 8: Analysis of material data (QML I) - Basics of using machine learning for processing experimental data. Week 9: Structure Classification (QML II) - Automatic analysis of phase diagrams and symmetries using algorithms. Week 10: Degradation Simulation - Modeling dynamic processes (e.g., hydrogen diffusion) beyond classical molecular dynamics. Week 11: Interpretation of results - Reading outputs from real processors and user error mitigation methods. Week 12: Semester project - Independent solution of an engineering assignment using cloud services. Week 13: The future of materials design - Integration of new methods into standard engineering workflows (CAD/CAE).
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Learning activities and teaching methods
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- Contact hours
- 52 hours per semester
- Undergraduate study programme term essay (20-40)
- 38 hours per semester
- Preparation for an examination (30-60)
- 30 hours per semester
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| prerequisite |
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| Knowledge |
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| Basic knowledge of crystallography and material structures. Basic knowledge of engineering mathematics and statistics. User knowledge of Python is required for working with SDK (Qiskit, PennyLane). |
| Skills |
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| Analytical thinking and the ability to algorithmize problems. |
| Competences |
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| N/A |
| Ability to learn independently and solve problems based on a framework assignment. |
| learning outcomes |
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| Knowledge |
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| Explain the fundamental differences between classical and quantum simulation and identify the types of tasks where these technologies offer potential advantages. Understand the principle of digital representation of materials and recognize the limitations of current hardware (the impact of noise on the accuracy of results). Gain an overview of the possibilities of machine learning for the classification of crystal structures. Be familiar with the range of current cloud computing services for materials research. |
| Skills |
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| Apply prepared simulation workflows for calculating energy stability using available software libraries (SDK). Select an appropriate strategy for solving optimization problems (e.g., batch arrangement) by choosing from a catalog of available algorithms. Interpret simulation results with regard to possible hardware error rates and propose standard procedures for error mitigation. Use data analysis methods to process outputs from material simulations. |
| Competences |
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| N/A |
| Make independent and responsible decisions when selecting tools based on general specifications. Assess the suitability of deploying new computing technologies compared to traditional methods in the context of engineering practice. Present results clearly and justify the choice of procedure used. |
| teaching methods |
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| Knowledge |
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| Interactive exercises linking quantum algorithm theory with examples from materials science practice. Demonstration of cloud computing on real hardware. |
| Skills |
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| Hands-on exercises with the Qiskit and PennyLane SDKs. Project-based teaching simulating a research task (from defining the Hamiltonian to visualizing the results). |
| Competences |
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| Independent study of recommended materials. Team consultations. |
| assessment methods |
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| Knowledge |
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| Ongoing tests to verify understanding of the principles of quantum algorithms. Final exam. |
| Skills |
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| Evaluation of practical tasks (e.g., success rate of steel property prediction, defect detection accuracy). Defense of a semester project focused on a specific material problem. |
| Competences |
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| Problem-solving skills, Presentation of project results. |
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Recommended literature
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Kostas Blekos, Dean Brand, Andrea Ceschini, Chiao-Hui Chou, Rui-Hao Li, Komal Pandya, Alessandro Summer. A Review on Quantum Approximate Optimization Algorithm and its Variants. 2024.
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M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, Patrick J. Coles. Variational Quantum Algorithms. 2021.
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Sam McArdle, Suguru Endo, Alan Aspuru-Guzik, Simon Benjamin, Xiao Yuan. Quantum computational chemistry. 2020.
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