Investigating and improving near-term quantum algorithms for machine learning and optimization | by QUTAC

The QUTAC working group Quantum Machine Learning and Optimization is investigating various approaches on how to leverage quantum computing for industrial use cases in these fields. We present extensions of the well-known Quantum Approximate Optimization Algorithm (QAOA) to reduce resource requirements on NISQ devices and benchmark their performance on the knapsack problem, which describes various use cases such as supply chain optimization or allocation of production tasks. Moreover, we will show a new application of a quantum generative model for tabular data.