# Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting

arXiv: https://arxiv.org/abs/2603.08072 · HTML: https://mlsanity.com/paper/2603.08072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy](https://mlsanity.com/paper/2204.12423/) (2022)
- [Deep Learning for Human Locomotion Analysis in Lower-Limb Exoskeletons: A Comparative Study](https://mlsanity.com/paper/2503.16904/) (2025)
- [Context-Gated Cross-Modal Perception with Visual Mamba for PET-CT Lung Tumor Segmentation](https://mlsanity.com/paper/2510.27508/) (2025)
- [Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates](https://mlsanity.com/paper/2602.17683/) (2026)
- [MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting](https://mlsanity.com/paper/2306.10356/) (2023)

## Built on by (most cited first, 1 of 1)

- [Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting](https://mlsanity.com/paper/2610.12148/) (2026)

## Abstract (arXiv)

Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings. The code is available at https://github.com/arco-group/quantum-ml.