论文标题
使用费米恩神经网络的高效和量子自适应机器学习
Efficient and quantum-adaptive machine learning with fermion neural networks
论文作者
论文摘要
经典的人工神经网络在机器学习应用中目睹了广泛的成功。在这里，我们提出了Fermion神经网络（FNN），其物理特性（例如状态的局部密度或条件电导）是输入作为初始层后的输出。与后传播相当，我们建立了有效的优化，该优化有资格在具有挑战性的机器学习基准上具有竞争性能。 FNN还直接适用于量子系统，包括具有相互作用的硬性系统，并提供原位分析而无需预处理或推定。在机器学习之后，FNN会精确确定拓扑阶段和紧急费用顺序。它们的量子性质还带来了各种优势：量子相关性具有更一般的网络连接性，并洞悉消失的梯度问题，量子纠缠为可解释的机器学习的新途径提供了新的途径。
Classical artificial neural networks have witnessed widespread successes in machine-learning applications. Here, we propose fermion neural networks (FNNs) whose physical properties, such as local density of states or conditional conductance, serve as outputs, once the inputs are incorporated as an initial layer. Comparable to back-propagation, we establish an efficient optimization, which entitles FNNs to competitive performance on challenging machine-learning benchmarks. FNNs also directly apply to quantum systems, including hard ones with interactions, and offer in-situ analysis without preprocessing or presumption. Following machine learning, FNNs precisely determine topological phases and emergent charge orders. Their quantum nature also brings various advantages: quantum correlation entitles more general network connectivity and insight into the vanishing gradient problem, quantum entanglement opens up novel avenues for interpretable machine learning, etc.
