The article introduces CoQS-LSTM, a Coherent Quantum Long Short-Term Memory Architecture designed for time‑series prediction. It presents a quantum approach that extends beyond classical methods, offering a novel framework for forecasting temporal data. The post was published on Towards Data Science.
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.
The paper presents a lightweight Quantum Prototypical Recurrent Unit (QPRU) that uses far fewer parameters than classical recurrent models like LSTM and GRU, as well as quantum variants such as QLSTM and QGRU. Despite its compactness, the QPRU matches state‑of‑the‑art forecasting performance. It offers structural and practical benefits, notably improved scalability and a reduced parameter count.
arXiv:2606. 24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing.
arXiv:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.
The paper demonstrates that a small quantum computer of polylogarithmic size can achieve large‑scale classification and dimension reduction on massive classical data, while any classical machine achieving the same performance would need exponentially larger size. It shows that classical machines even if exponentially larger still require superpolynomially more samples and time. The authors provide evidence from real‑world applications such as single‑cell RNA sequencing and movie review sentiment analysis, achieving four to six orders of magnitude reduction in size with fewer than 60 logical qubits.
The paper introduces a recursive quantum long short-term memory (QLSTM) architecture and compares it to a standard QLSTM for one-step-ahead daily temperature forecasting. Using Toronto weather data and identical training settings, the recursive model consistently reaches near-optimal test loss earlier, achieves lower mean absolute error and root mean squared error, and shows a smaller generalization gap across input windows of 8, 16, and 32 days over 20 random seeds. These findings suggest that recursive quantum feature transformations can enhance stability and out-of-sample performance in compact hybrid quantum–classical temporal models.
Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity.
arXiv:2607. 24399v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data.
arXiv:2606. 24932v1 Announce Type: cross Abstract: Recent advances in quantum computing and machine learning have motivated the development of quantum models for sequential data processing.
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.
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.
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.