A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables
This work addresses forecasting problems for domains such as energy and finance by enabling efficient transfer learning with quantum models, though it is incremental as it builds on existing quantum neural network approaches.
The study introduced continuous- and discrete-variable quantum neural network models for transfer learning in forecasting tasks, achieving strong performance by training on a single dataset and applying frozen parameters to multiple problems like energy consumption and cryptocurrency prediction with minimal fine-tuning.
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.