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 as a benchmark, the authors compare a complex Transformer model with a simple linear DLinear model across regimes from ergodic behavior to quantum many-body scarring. They find that while the Transformer works well in ergodic regimes, its performance degrades near scarred states, whereas DLinear remains accurate across all initial states, indicating that complex quantum dynamics can be forecasted 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: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: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:2608. 07363v1 Announce Type: new Abstract: Forecasting non-stationary time series remains difficult due to long-range dependencies, local volatility bursts, structural shifts, and nonlinear oscillatory behaviors.
By Junkai Lin, Siqi Hou, Raymond Lee
arXiv:2608. 19497v1 Announce Type: new Abstract: We characterize the learning dynamics of a compact hybrid quantum forecasting model through comparison with a structurally aligned classical baseline.
By Sandra Leticia Ju\'arez-Osorio, Jorge I. Hernandez-Martinez, Jesus Ivan Ruiz-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
arXiv:2602.22061v3 Announce Type: replace-cross
Abstract: Generative models for quantum data pose significant challenges but hold immense potential in fields such as chemoinformatics and quantum phys...
By Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima
arXiv:2607. 16358v1 Announce Type: cross Abstract: This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F).
By Sanjay Chakraborty, Fredrik Heintz
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
arXiv:2605. 18333v2 Announce Type: replace-cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings.
By Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan, Muhammad Kashif, Muhammad Shafique
The paper develops a theoretical framework for quantum extreme learning machines (QELMs) using the Pauli transfer matrix (PTM) formalism. It shows how encoding, reservoir dynamics, and measurement observables jointly determine the nonlinear information processing capacity of a QELM, and provides interpretable diagnostics that can guide model design. The authors apply their analysis to forecasting nonlinear dynamical systems, demonstrating that a trained QELM’s readout closely reproduces the underlying flow map at leading orders.
By Markus Gross, Hans-Martin Rieser