# A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables

arXiv: https://arxiv.org/abs/2503.07633 · HTML: https://mlsanity.com/paper/2503.07633/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Continuous-variable quantum neural networks](https://mlsanity.com/paper/1806.06871/) (2018)
- [A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage](https://mlsanity.com/paper/2006.14815/) (2020)
- [Quantum Neural Networks: Concepts, Applications, and Challenges](https://mlsanity.com/paper/2108.01468/) (2021)
- [On Physics-Informed Neural Networks for Quantum Computers](https://mlsanity.com/paper/2209.14754/) (2022)
- [Quantum computing overview: discrete vs. continuous variable models](https://mlsanity.com/paper/2206.07246/) (2022)
- [Continuous Variable Quantum MNIST Classifiers](https://mlsanity.com/paper/2204.01194/) (2022)
- [Photonic Quantum Computers](https://mlsanity.com/paper/2409.08229/) (2024)

## Abstract (arXiv)

This study introduces simple yet effective continuous- and discrete-variable quantum neural network (QNN) models as a transfer-learning approach for forecasting tasks. The CV-QNN features a single quantum layer with two qubits to establish entanglement and utilizes a minimal set of quantum gates, including displacement, rotation, beam splitter, squeezing, and a non-Gaussian cubic-phase gate, with a maximum of eight trainable parameters. A key advantage of this model is its ability to be trained on a single dataset, after which the learned parameters can be transferred to other forecasting problems with little to no fine-tuning. Initially trained on the Kurdistan load demand dataset, the model's frozen parameters are successfully applied to various forecasting tasks, including energy consumption, traffic flow, weather conditions, and cryptocurrency price prediction, demonstrating strong performance. Furthermore, the study introduces a discrete-variable quantum model with an equivalent 2- and 4-wire configuration and presents a performance assessment, showing good but relatively lower effectiveness compared to the continuous-variable model.