TY - GEN
T1 - The realization of quantum complex-valued backpropagation neural network in pattern recognition problem
AU - Mitrpanont, J. L.
AU - Srisuphab, A.
N1 - Publisher Copyright:
© 2002 Nanyang Technological University.
PY - 2002
Y1 - 2002
N2 - The paper presents the approach of the quantum complex-valued backpropagation neural network or QCBPN. The challenge of our research is the expected results from the development of the quantum neural network using complex-valued backpropagation learning algorithm to solve classification problems. The concept of QCBPN emerged from the quantum circuit neural network research and the complex-valued backpropagation algorithm. We found that complex value and the quantum states share some natural representation suitable for the parallel computation. The quantum circuit neural network provides a qubit-like neuron model based on quantum mechanics with quantum backpropagation-learning rule, while the complex-valued backpropagation algorithm modifies standard backpropagation algorithm to learn complex number pattern in a natural way. The quantum complex-valued neuron model and the QCBPN learning algorithm are described. Finally, the realization of the QCBPN is exploited with a simple pattern recognition problem.
AB - The paper presents the approach of the quantum complex-valued backpropagation neural network or QCBPN. The challenge of our research is the expected results from the development of the quantum neural network using complex-valued backpropagation learning algorithm to solve classification problems. The concept of QCBPN emerged from the quantum circuit neural network research and the complex-valued backpropagation algorithm. We found that complex value and the quantum states share some natural representation suitable for the parallel computation. The quantum circuit neural network provides a qubit-like neuron model based on quantum mechanics with quantum backpropagation-learning rule, while the complex-valued backpropagation algorithm modifies standard backpropagation algorithm to learn complex number pattern in a natural way. The quantum complex-valued neuron model and the QCBPN learning algorithm are described. Finally, the realization of the QCBPN is exploited with a simple pattern recognition problem.
UR - https://www.scopus.com/pages/publications/84965077460
U2 - 10.1109/ICONIP.2002.1202213
DO - 10.1109/ICONIP.2002.1202213
M3 - Conference contribution
AN - SCOPUS:84965077460
T3 - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
SP - 462
EP - 466
BT - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
A2 - Rajapakse, Jagath C.
A2 - Yao, Xin
A2 - Wang, Lipo
A2 - Fukushima, Kunihiko
A2 - Lee, Soo-Young
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Neural Information Processing, ICONIP 2002
Y2 - 18 November 2002 through 22 November 2002
ER -