Programme outline
Learning objectives
- Describe the key principles of quantum-inspired algorithms and quantum algorithms and the role of effective usage in classical-quantum hybrid environment
- Explain the relationship between quantum-inspired algorithms, mathematical models as well as the differences, and when to use quantum algorithms
- Assess the effectiveness of quantum algorithms and quantum approach for businesses and industries
- Understand various quantum technologies and quantum algorithms to inspire creative solutions
- Refine participants’ existing Artificial Intelligence (AI) models and start to evaluate quantum approach for their current computation needs
Day 1
- General introduction and course content
- Introduction to AI
Current abilities and cost (computation and energy cost)
Applications of neural networks - Basics in quantum computing
Qubits / measurement / interference / memory / hints of advantages - Introduction to quantum inspired (basics in tensor networks)
Relevant example with tensor networks / fast fourier transformation / compression of neural networks and large language models - Basic quantum algorithms on quantum simulator
Deutsch Josza Algorithm a toy example/ Grover Search for faster database searching / Quantum version of Fourier Transformations - Assessment
Day 2
- Quantum computing hardware
Qubit implementations (superconducting / neutral atoms / ions / photons) / error mitigation / scaling / road maps - Overview of global efforts in quantum technology
- Quantum algorithms for industry
Relevant applications
Shor factorization algorithm / variation quantum Eigensolver for optimisation / Harrow–Hassidim–Lloyd algorithm for solving linear systems of equations - Use cases for quantum-inspired algorithms
Applications in physics / machine learning / image recognition / finance - Use cases for quantum algorithms
Applications in chemistry and biology industries / optimisation for processes / applications in the finance sector
Assessment
- Online quiz