Publications
2025
Optimization driven quantum circuit reduction
Bodo Rosenhahn, Tobias 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 process is also known as transpilation and due to decoherence, it is mandatory to keep quantum circuits as short as possible, without affecting functionality. In this work we propose three different transpilation approaches, based on a localized term-replacement scheme, to substantially reduce circuit lengths while preserving the unitary operation implemented by the circuit.
2024
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. anomaly detection, once such a mapping has been learned. In this work we introduce Normalizing Flows for Quantum architectures, describe how to model and optimize such a flow and evaluate our method on example datasets.
2023
Monte Carlo Graph Search for Quantum Circuit Optimization
Bodo Rosenhahn, Tobias Osborne
The building blocks of quantum algorithms and software are quantum gates, with the appropriate combination of quantum gates leading to a desired quantum circuit. Deep expert knowledge is necessary to discover effective combinations of quantum gates to achieve a desired quantum algorithm for solving a specific task. This is especially challenging for quantum machine learning and signal processing. This work proposes a quantum architecture search algorithm which is based on a Monte Carlo graph search and measures of importance sampling.