Machine-Learning-Supported Ion and Rydberg Quantum Computing and Quantum Computing Based on Superconducting Qubits and Neutral Atoms

P3a

In this project, we will utilize machine learning to optimize the implementation of quantum computers in ion traps and Rydberg systems. The central aim is to achieve the optimal implementation of quantum algorithms in these systems. This involves incorporating the specific properties of the implementation into the optimization process, such as determining the speed and precision of each operation. Collaboration with TUHH on machine learning is being planned.

P3b

In this project, we will theoretically develop advanced quantum computer implementations based on both superconducting and topological qubits, as well as neutral atoms. Superconducting qubits can be realized as Transmon or Flux qubits, while topological qubits can be hybridized from Majorana states. Cold, neutral atoms represent an established platform for quantum simulation, which we will now further develop with a focus on quantum computing. For these systems, we will develop optimal implementations, requiring an understanding of their dissipative properties. Collaboration with scientists at Station Q is being planned. Atomtronic systems will be concurrently investigated to leverage synergies.