Real-World Optimization on Quantum Computers

Developing real-world optimization techniques on quantum computers.

Quantum computing (QC) promises to revolutionize the present form of computing by solving complex problems that traditional computers would never crack. Moreover, the rising number of QC platforms reflects QC's potential to solve highly complex safety and mission-critical 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 in addition to executing these programs on real quantum computers. Finally, cloud-based services, such as Amazon, provide access to various types of quantum computers and simulators. Many real-world problems are optimization problems. To this end, the thesis can focus on developing optimization techniques for quantum computers (e.g., based on quantum annealers and quantum approximate optimization algorithm (QAOA)) 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 Geographical Modelling and Analysis, Geographical Database Systems, Geographical Data Mining, Climate Modelling Biostatistics, Biophysics, Bioinformatics, Computational Neuroscience, Energy Physics, Building Structures, Building Performance Simulation, Economics, Civil Engineering, or Others.


Learning cutting-edge quantum computing technology - Applying quantum computing to a problem of your area of expertise

Learning outcome

  • Programming quantum computers


  • Programming skills in Python


  • Shaukat Ali


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