量子力学为信息处理带来新的方法与机遇。近几年量子计算与机器学习的快速发展催生了一个新的研究前沿——量子机器学习。在这篇博士论文中，我们介绍了量子机器学习的架构优化，以及量子自旋链中的信息处理。我们首先回顾了量子计算、凝聚态物理和机器学习领域的一些基本概念。随后我们介绍了量子机器学习中一些著名的量子算法和量子模拟技术。在嘈杂中等规模量子时代，搜索具有较小深度的、性能良好的量子线路对于量子机器学习实验演示至关重要。我们引入了一种量子神经进化算法，该算法可以自动地为不同的机器学习任务找到接近最优的量子神经网络。为展示此算法的优势，我们设计了一个量子分类器进行手写字体和拓扑态的识别。我们的结果展示了神经进化算法在量子架构搜索中的巨大潜力。远距离无耗散地传输信息和能量是物理学的重要研究方向之一。我们详细讨论了一维量子Ising-XY-Ising 自旋链的自旋输运。通过运用对称性分析、数值模拟和解析方法，我们揭示了几种与涌现的马约拉纳费米子有关的分数自旋约瑟夫森效应。我们进一步分析了自旋电流对自旋纠缠的影响。我们提出使用腔量子电动力学装置中的色散读出技术来探测自旋约瑟夫森效应。我们可以通过操控腔中的光子来进行量子信息处理。最后，我们展望了一些新的量子机器学习架构和算法。

Quantum mechanics provides new methods and opportunities for information processing. Recent rapid developments in quantum computing and machine learning have spawned a new cutting-edge research frontier—quantum machine learning. In this doctoral dissertation, we introduce the architecture optimization for quantum machine learning, together with the information processing in quantum spin chains.We first review some basic concepts in the field of quantum computation, condensed matter physics, and machine learning. We then introduce some renowned quantum algorithms and quantum simulation techniques for quantum machine learning. In the noisy intermediate-scale quantum era, searching a well-performing quantum circuit with a smaller depth is of crucial importance for the experimental demonstration of quantum machine learning. We introduce a quantum neuroevolution algorithm that autonomously finds near-optimal quantum neural networks for different machine learning tasks. To benchmark the effectiveness, we design a quantum classifier for the classification of both handwritten digits and topological states. Our results showcase the vast potential of neuroevolution algorithms in quantum architecture search.The ability to transmit information and energy without dissipation over large distances is one of the important research directions in physics. We specifically investigate the spin transport through a one-dimensional quantum Ising-XY-Ising spin link. Using a combination of symmetry analysis, numerical calculations, and analytical approaches, we have revealed several types of fractional spin Josephson effects that pertain to the emergent Majorana fermions. We further analyzed the entanglement between pairs of spins in the presence of spin currents. We proposed to use a cQED setup for detecting the spin Josephson effects by dispersive readout methods. This detection proposal could be used to control the quantum information processing through photons in cQED setups. Finally, we provide an outlook for some new architectures and algorithms of quantum machine learning.