As the quantum counterparts to the classical artificial neural networks
underlying widespread machine-learning applications, unitary-based quantum
neural networks are active in various fields of quantum computation. Despite
the potential, their developments have been hampered by the elevated cost of
optimizations and difficulty in realizations. Here, we propose a quantum neural
network in the form of fermion models whose physical properties, such as the
local density of states and conditional conductance, serve as outputs, and
establish an efficient optimization comparable to back-propagation. In addition
to competitive accuracy on challenging classical machine-learning benchmarks,
our fermion quantum neural network performs machine learning on quantum systems
with high precision and without preprocessing. The quantum nature also brings
various other advantages, e.g., quantum correlations entitle networks with more
general and local connectivity facilitating numerical simulations and
experimental realizations, as well as novel perspectives to address the
vanishing gradient problem long plaguing deep networks. We also demonstrate the
applications of our quantum toolbox, such as quantum-entanglement analysis, for
interpretable machine learning, including training dynamics, decision logic
flow, and criteria formulation.

我们提出一种基于费米模型的量子神经网络，其物理特性作为输出，并建立了与反向传播相媲美的高效优化，在具有挑战性的经典机器学习基准上具有竞争力的准确度，并且在量子系统上实现高精度且不需要预处理的机器学习，此外研究结果可用于量子纠缠分析和可解释的机器学习。