Dataset and Python Code of "High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins"

This repository contains the experimental data and Python code associated with the article "High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins".In this work, we experimentally implement quantum reservoir computing (QRC) using a correlated spin system, achieving high performance on both the NARMA benchmark task and a real-world weather prediction task. Code files NARMA task: expr_NARMA.ipynb — Processes experimental data for the NARMA task. simu_9spin_NARMA_expr.ipynb — Simulates how a 9-spin QRC processes the NARMA task in the experimental setting. simu_9spin_process.ipynb — Processes the results obtained from simu_9spin_NARMA_expr.ipynb. simu_CRC.ipynb — Simulates a classical spin reservoir computer (CRC) for the NARMA task. Weather prediction task: expr_weather.ipynb — Processes experimental data for the weather prediction task. esn_hyperparam_gridsearch — Performs grid search to determine hyperparameters of the echo state network (ESN). esn_process — Runs the ESN using the selected hyperparameters, and performs weather prediction. Other simulations: simu_STM.ipynb — Numerically investigates the effect of different relaxation mechanisms on QRC performance in a short-term memory (STM) task. Note: Before running the code, extract each compressed archive into a folder with the same name. Data files expr_data — All experimental data, including FID signals and spectra. simu_narma_data — Observables and states from NARMA simulations, as well as CRC states. weather_dataset — Dataset for the weather prediction task. esn_data — ESN prediction results for the weather dataset. esn_svr — Results from hyperparameter search for SVR post-processing. stm_data — Expectation values from quantum models in the STM task.