The paper investigates whether machine learning can predict chaotic quantum many-body dynamics by treating it as a time‑series forecasting problem. Using a few‑qubit PXP chain, the authors compare a nonlinear Transformer model with a simple linear DLinear model across regimes from ergodic to scarred dynamics. They find that while the Transformer performs well in ergodic regimes, its accuracy drops near scarred states, whereas DLinear maintains high accuracy across all regimes, indicating that complex quantum dynamics can be forecasted accurately with a simple linear mapping.
By Francesco Perciavalle, Agostino Gallo, Francesco Plastina, Gianluigi Greco, Nicola Lo Gullo, Carlo Adornetto
The paper evaluates hybrid quantum‑classical machine learning for predicting reduced‑order spatiotemporal brain deformation fields. Using Proper Orthogonal Decomposition to compress high‑dimensional displacement data, the authors compare static temporal‑to‑latent regression and autoregressive latent forecasting models. Classical neural networks outperform all quantum variants, though enhanced quantum circuits improve over minimal ones, indicating that classical architectures still hold a clear advantage in fidelity and stability for this task.
By Tao Liu, Ge He, Dongyu Liang, Wujie Wen
arXiv:2606. 15213v1 Announce Type: cross Abstract: Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors.
By Jonathan H. A. de Carvalho, Filipe C. de L. Duarte, Fernando M. de Paula Neto, Paulo S. G. de Mattos Neto
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
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.
arXiv:2602. 06699v2 Announce Type: replace-cross Abstract: We propose a variational quantum implementation of self-attention (QSA)-the core operation in transformers and large language models-which predicts future elements of a sequence by forming overlap-weighted combinations of past data.
By Alessio Pecilli, Matteo Rosati