Real-world optimization and machine learning on quantum computers
Up for quantum-powered solutions to real-world optimization or machine learning?
It is increasingly become evidence that Quantum computing (QC) holds the potential to completely revolutionize the landscape of computing. By harnessing the principles of quantum mechanics, it can solve intricate problems, where the computational power of classical computers reach their limit. Moreover, the rising number of QC platforms underpins QC's potential to solve highly complex problems. Several programming languages are available now to program quantum computers, such as Qiskit by IBM, Q# by Microsoft, and Cirq by Google. One can write programs in these languages, and they can be executed on the provided quantum computer simulators, or on real quantum computers. Finally, cloud-based services, such as Amazon Braket, provide access to various types of quantum computers and simulators. Many real-world problems are optimization problems. To this end, these theses can focus on developing optimization techniques for quantum computers with quantum approximate optimization algorithms to solve a real-world problem of your choice. Such techniques will be implemented in suitable quantum programming languages to execute either on real or simulated quantum computers. The application area could be of your choice, such as finance, software engineering, energy optimization, scheduling, route planning, or others. In addition, there is possibility to explore the new area of Quantum Machine Learning (QML) in this topic.
Goal
- Learning cutting-edge quantum computing technology
- Leveraging quantum computing to address a challenge within your domain of expertise
Learning outcome
- Basics of quantum computing
- Programming quantum computers
Qualifications
- Programming skills in Python
- Nice to have: Background in quantum computing
Supervisors
- Shaukat Ali
