A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for
  Non-Ideal Training Sets
Wenjie Liu Peipei Gao Yuxiang Wang Wenbin Yu Maojun Zhang
In order to solve the problem of non-ideal training sets (i.e., the
less-complete or over-complete sets) and implement one-iteration learning, a
novel efficient quantum perceptron algorithm based on unitary weights is
proposed, where the singular value decomposition of the total weight matrix
from the training set is calculated to make the weight matrix to be unitary.
The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows
that our algorithm can accurately implement arbitrary quantum gates within one
iteration. The performance comparison between our algorithm and other quantum
perceptron algorithms demonstrates the advantages of our algorithm in terms of
applicability, accuracy, and availability. For further validating the
applicability of our algorithm, a quantum composite gate which consists of
several basic quantum gates is also illustrated.