We apply sonification strategies and quantum computing to the analysis of an episode of seizure. We first sonify the signal from a selection of channels (from real ECoG data), obtaining a polyphonic sequence. Then, we propose two quantum approaches to simulate a similar episode of seizure, and we sonify the results. The comparison of sonifications can give hints on similarities and discrepancies between real data and simulations, helping refine the \textit{in silico} model. This is a pioneering approach, showing how the combination of quantum computing and sonification can broaden the perspective of real-data investigation, and helping define a new test bench for analysis and prediction of seizures.

翻译：本研究将声学化策略与量子计算应用于癫痫发作事件的分析。首先，我们对选定通道（源自真实ECoG数据）的信号进行声学化处理，生成多音轨序列。随后，我们提出两种量子模拟方法以模拟类似癫痫发作事件，并对模拟结果进行声学化处理。通过对比声学化结果，可以揭示真实数据与模拟数据之间的相似性与差异性，从而有助于优化\textit{in silico}模型。这种开创性方法展示了量子计算与声学化技术的结合如何拓宽真实数据研究的视角，并为癫痫发作的分析与预测建立新型测试基准。