Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Toward Trainability of Deep Quantum Neural Networks   Quantum Neural Networks (QNNs) with random structures have poor trainability
due to the exponentially vanishing gradient as the circuit depth and the qubit
number increase. This result leads to a general belief that a deep QNN will not
be feasible. In this work, we provide the first viable solution to the
vanishing gradient problem for deep QNNs with theoretical guarantees.
Specifically, we prove that for circuits with controlled-layer architectures,
the expectation of the gradient norm can be lower bounded by a value that is
independent of the qubit number and the circuit depth. Our results follow from
a careful analysis of the gradient behaviour on parameter space consisting of
rotation angles, as employed in almost any QNNs, instead of relying on
impractical 2-design assumptions. We explicitly construct examples where only
our QNNs are trainable and converge, while others in comparison cannot.

33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
12
The categories of this paper are: nucl-th