Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implementing perceptron models with qubits   We propose a method for learning a quantum probabilistic model of a
perceptron. By considering a cross entropy between two density matrices we can
learn a model that takes noisy output labels into account while learning. A
multitude of proposals already exist that aim to utilize the curious properties
of quantum systems to build a quantum perceptron, but these proposals rely on a
classical cost function for the optimization procedure. We demonstrate the
usage of a quantum equivalent of the classical log-likelihood, which allows for
a quantum model and training procedure. We show that this allows us to better
capture noisyness in data compared to a classical perceptron. By considering
entangled qubits we can learn nonlinear separation boundaries, such as XOR.

9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th