A quantum reservoir computing approach to quantum stock price forecasting in technology-markets
TL;DR
The QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.
Abstract
We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading prices of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025 (exclusive), as well as minute-by-minute trading prices during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding 86%. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.