10 papers · 1 filter
When Can Quantum Extreme Learning Machines Replace Quantum Reservoirs?
Markus Baumann, Gerhard Stenzel, Jonas Stein +1
Quantum reservoir computers (QRCs) retain information from earlier inputs, whereas reset-window quantum extreme learning machines (QELMs) start afresh and receive only a recent inp…
The Organization of Environmental Coupling Shapes What Quantum Reservoirs Remember
Markus Baumann, Itamar Fink, Johannes Wittmann +2
For an open quantum reservoir, how the system forgets is part of how it computes. Quantum reservoir computing processes input streams with fixed quantum dynamics and trains only a…
Implicit Differentiation for Measurement-Efficient Bilevel Quantum-Classical Optimization
Tobias Rohe, Markus Baumann, Federico Harjes Ruiloba +3
Quantum optimization has shown promising results for quadratic unconstrained binary optimization (QUBO) problems. Real-world applications, however, often involve polynomial coeffic…
Symmetry Alone Is Not an Ansatz: Task-Aligned Interactions in Equivariant Quantum Circuits
Markus Baumann, Claudia Linnhoff-Popien, Jonas Stein
The success of variational quantum learning models crucially depends on choosing parametrizations that reflect the structure of the problem at hand. Symmetries provide one of the c…
Where a Quantum Reservoir Works: A Transferable Operating Band
Markus Baumann, Itamar Fink, Johannes Wittmann +2
In quantum reservoir computing, a fixed quantum system transforms an input signal, while learning reduces to training a simple linear readout on its measured outputs. Since the qua…
Exploiting Symmetry in Quantum Reservoir Computing
Markus Baumann, Michael Poppel, Thomas Gabor +3
Quantum reservoir computing (QRC) uses a quantum processor without training it. The input is encoded into a quantum state, a fixed random circuit evolves it, selected observables a…
Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ansätze for Combinatorial Optimization
Tobias Rohe, Markus Baumann, Federico Harjes Ruiloba +3
Variational quantum algorithms (VQAs) for combinatorial optimization routinely employ entangling gates as a default design choice, yet the role of entanglement, in its amount and s…
Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
Michael Poppel, David Bucher, Maximilian Zorn +5
Variational quantum circuits with angle encoding implement truncated Fourier series, and architectures arranging qubits with encoding layers each -- sharing encoding budget…
Parity Supervision as a Driver of Generalization in Quantum Generative Modeling
Markus Baumann, Daniel Hein, Steffen Udluft +3
Generative models learn probability distributions in order to produce new samples beyond a finite training set. Their usefulness therefore depends on assigning probability to valid…
From Quantum Shots to Training Data: Reorganizing Measurement Records in Quantum Machine Learning
Markus Baumann, Maximilian Zorn, Thomas Gabor +2
Between keeping every shot as a noisy training example and averaging all shots into one clean feature lies an entire spectrum of data organizations that quantum machine learning us…