Quantum Computing
We conduct research in quantum computing at the interface of fundamental science and practical applications. Our work integrates fundamental quantum phenomena into the development of algorithms for quantum simulation, quantum machine learning, optimization, and cybersecurity.
Real-world quantum processors are never perfectly isolated from their environment. They are affected by errors, decoherence, and dissipation, and they undergo measurements and reset operations. We develop theoretical and numerical methods to describe the resulting non-unitary dynamics and to mitigate their impact. A central focus of our research is on quantum walks and so-called first-hitting-time problems: How long does it take for a quantum system to reach a given target state for the first time? We investigate this question through theoretical analysis, numerical simulations, and experimental studies using mid-circuit measurements on real quantum hardware. This research opens up potential applications in graph and network analysis as well as quantum search algorithms.
Another research direction is quantum reservoir computing, where we exploit the natural dynamics of quantum systems for machine learning and time-series forecasting. We also investigate stochastic resetting, transferring a well-established concept from classical statistical physics to the quantum domain. Here, each reset operation serves as a novel control mechanism that can enable genuinely quantum speedups, for example in optimization algorithms.
