Fraunhofer Institute for Applied Solid State Physics IAF
Quantum algorithms for machine learning are among the most promising applications of quantum computing. However, these algorithms require many error-corrected qubits to achieve a quantum advantage. On the noisy intermediate scale quantum (NISQ) processors available today, however, the realization of error correction methods is not yet technically possible. This makes the research of so-called NISQ algorithms for quantum machine learning necessary.
With the aim of enabling practical applications of machine learning with quantum computing and supporting the industry in this regard, the project will develop protocols, libraries and algorithms for various quantum computing platforms and combine current scientific findings in quantum computing with those of machine learning. The innovation is in the symbiosis of hardware and software, which contributes to the use of machine learning for scientific optimization.
