本文提出一种基于伊辛演化的储层计算模型，旨在提高传统储层计算模型的性能和鲁棒性。该模型主要使用了伊辛演化的思想来对传统的储层模型进行优化。该模型根据伊辛演化规则可以对数据进行有效处理，使得模型对于数据的泛化性、鲁棒性更高。我们通过手写数字识别任务来测试该模型性能，并与传统模型进行对比，验证了基于伊辛演化的储层计算模型在性能上的优势。

此外该模型具有在物理器件上实现的潜力。由于储层计算的动力学特性，储层计算可以广泛地在物理模型上进行实现。因此将该类模型使用光学器件进行实现，并用于对人工智能计算加速上具有很大前景。

基于伊辛演化的储层计算还有待更深层次的研究和优化，提高其性能、了解其特性与优势。此外，将其使用伊辛相干机模型实现也是未来的一个课题。

A reservoir computing model based on Ising evolution is proposed in this paper, aimed at improving the performance of traditional reservoir computing. The model primarily utilizes the principal of Ising evolution to optimize traditional models. According to the rules of Ising evolution, the model can effectively process data, resulting in higher robustness of the model. The model’s performance was testes by a handwriting digit recognition task using MNIST dataset and compared with a traditional reservoir computing model. These tests demonstrated the advantages of the Ising-evolution-based reservoir computing model.

Additionally, the model has the potential to be implemented on physical devices. Due to the dynamics of reservoir computing, reservoir computing models can be widely implemented in various physical systems. Therefore, implementing reservoir computing using optical devices has great prospects for accelerating AI computing.

In summary, the proposed reservoir computing based on Ising evolution provides a novel approach to optimize the traditional models and improve the performance. It also has the potential to be implemented on Ising coherent machines and other physical devices. And further research is needed to explore its properties and enhance the performance of Ising-evolution-based reservoir computing.