2 citations · 5 across the 20 of their papers we have counts for
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Reinforcement learning for ion shuttling on trapped-ion quantum computers
Maximilian Schier, Lea Richtmann, Christian Staufenbiel +4
Scalable trapped-ion quantum computing is commonly realized with modular chips that feature distinct zones with specific functionalities, such as storage, state preparation, and ga…
Stochastic Neural Networks for Quantum Devices
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stocha…
Neural Guided Sampling for Quantum Circuit Optimization
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the l…
Optimization Driven Quantum Circuit Reduction
Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
Implementing a quantum circuit on specific hardware with a reduced available gate set is often associated with a substantial increase in the length of the equivalent circuit. This…
Quantum Normalizing Flows for Anomaly Detection
Bodo Rosenhahn, Christoph Hirche
A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g.…