Quantum reservoir computers (QRCs) retain information from earlier inputs, whereas reset-window quantum extreme learning machines (QELMs) start afresh and receive only a recent input window. We establish when this window can replace the reservoir's memory. Under our stated assumptions and with exact expectation values,...
M. Baumann, Gerhard Stenzel, Jonas Stein et al.· 0 citations
This work analyzes the effect of encoding discontinuities on diversity-driven discovery and introduces two representation refinements: a smooth, continuous mechanism for gate selection and a factorized representation that separates structural discovery from continuous parameter optimization.
Maximilian Zorn, Jonas Stein, Philipp Altmann et al.· Proceedings of the 32nd ACM...· 0 citations
In the Quantum Approximate Optimization Algorithm (QAOA), the entanglement topology, where qubit pairs are connected by two-qubit gates, is conventionally set equal to the edge set of the problem graph. This coupling ties circuit design to explicit problem knowledge and may not yield the most trainable circuit under li...
Tobias Rohe, Federico Harjes Ruiloba, Markus Baumann et al.· Frontiers in Quantum Science...· 0 citations
We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive next-token interface of a classical transformer, but replaces attention and feed-forward sublayers with variational quantum encoder blocks, connector circuits, decoder bloc...
Julian Hager, Michael Kölle, Gerhard Stenzel et al.· 0 citations
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 linear readout. Dissipation can make old inputs fade, but prior studies commonly fix the environmental process and tune only its strength. H...
M. Baumann, Itamar Fink, Johannes Wittmann et al.· 0 citations
This work proposes a bilevel optimization model for diagonal cost Hamiltonians where coefficients depend on a tunable outer parameter and develops correlator-reuse implicit differentiation (CR-ID), which obtains outer gradients by reusing quantum measurements already collected during inner energy estimation, requiring...
Tobias Rohe, M. Baumann, Federico Harjes Ruiloba et al.· 0 citations
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