Bonn 2025 – scientific programme
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QI: Fachverband Quanteninformation
QI 2: Quantum Machine Learning I
QI 2.3: Talk
Monday, March 10, 2025, 11:30–11:45, HS VIII
Expressive power of reservoir-based quantum machine learning — •Nils-Erik Schütte1,2, Niclas Götting2, Hauke Müntinga1, Meike List1,3, and Christopher Gies2 — 1German Aerospace Center, Institute for Satellite Geodesy and Inertial Sensing, Bremen, Germany — 2Institut für Physik, Fakultät V, Carl von Ossietzky Universität Oldenburg — 3University of Bremen
Quantum machine learning merges quantum computing and artificial intelligence, two transformative technologies for data processing. While gate-based quantum computing employs precise unitary operations on qubits, noisy intermediate-scale quantum (NISQ) devices face limitations in implementing high-depth circuits, yet remain promising for machine learning applications. In contrast, quantum reservoir computing (QRC) leverages physical systems as quantum neural networks, relying on Hamiltonian dynamics rather than controlled gate operations, with learning performed at the output layer. Despite their differing foundations, these approaches share connections and can be formally mapped onto each other.
We discuss this analogy by realizing a transverse-field Ising model on a gate-based quantum computing architecture. We quantify expressivities of either approach and explore the potential of gate-based quantum computers over QRC that rely on quantum circuit design and the possibility to optimize the circuits for specific tasks. Furthermore, we discuss the balance of the influence of the input encoding and the complexity of the reservoir on the output functions that a QRC approach has access to.
Keywords: Quantum reservoir computing; Quantum machine learning