arXiv:2608. 19306v1 Announce Type: cross Abstract: Given a set of input states, we consider the task of predicting the expectation value of a Pauli observable at the output of an unknown quantum evolution, using only a limited number of measurements.
By Jonas J\"ager, Yaroslav Khmelnitskiy, Paolo Braccia, Artur Miroszewski, Diego Garc\'ia-Mart\'in, M. Cerezo, Piotr Czarnik
arXiv:2607. 07978v1 Announce Type: cross Abstract: Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it.
By Tushar Pandey
arXiv:2604. 08277v3 Announce Type: replace-cross Abstract: We present QARIMA, a quantum state-similarity-based reconstruction of the classical ARIMA modelling pipeline.
By Nishikanta Mohanty, Bikash K. Behera, Badshah Mukherjee, Pravat Dash, Giuseppe Sergioli, Roberto Giuntini
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
By Chi-Sheng Chen, En-Jui Kuo
arXiv:2606. 19947v1 Announce Type: cross Abstract: Reliable quantum control in the presence of decoherence requires policies that combat the effect of environmental noise on the controlled dynamics.
By Merijn Moody, Zier Mensch, Miranda C. N. Cheng, Peter G. Bolhuis, Max Welling
Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it. This makes it cheap to train and free of the optimisation problems that affect many quantum machine-learning models.