A quantum neural network with efficient optimization and interpretability. (arXiv:2211.05793v1 [quant-ph])

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.