The impact of noise on the Variational Quantum Eigensolver & quantum-classical convolutional neural networks for radiological image classification
Miriam Lorenz and Andrea Matic, Fraunhofer IKS institute
Theory Seminar at Herbert-Walther lecture hall and Zoom
Wednesday, December 14th, 2022 at 11:30am (MEZ)
Hybrid algorithms are a promising way for realizing near-term applications of quantum computing. In this talk, we will present their usage for two different application areas: Quantum Simulation and Quantum Machine Learning (QML). For the former, we investigated how hardware noise affects the simulation results of the Variational Quantum Eigensolver (VQE). This is demonstrated on the example of calculating the ground state energy of the hydrogen molecule. In the second part of the talk we will present how QML can be used for radiological image classification tasks. For this purpose, we studied the performance of hybrid quantum-classical convolutional neural networks (QCCNNs) and obtained promising results.