TY - GEN
T1 - Energy dissipation effect on a quantum neural network
AU - Kinjo, Mitsunaga
AU - Sato, Shigeo
AU - Nakajima, Koji
PY - 2008/10/23
Y1 - 2008/10/23
N2 - A quantum neural network based on the adiabatic quantum computation is one of candidates to overcome the difficulty for developing a quantum computation algorithm. Furthermore, by applying energy dissipation to the adiabatic quantum computation, an application of the quantum neural network is expanded. In this paper, we discuss effect which arises from the utilization of energy dissipation on a quantum neural network. Preliminary results which have been shown by numerical simulations indicate an availability of the energy dissipation for the quantum neural network.
AB - A quantum neural network based on the adiabatic quantum computation is one of candidates to overcome the difficulty for developing a quantum computation algorithm. Furthermore, by applying energy dissipation to the adiabatic quantum computation, an application of the quantum neural network is expanded. In this paper, we discuss effect which arises from the utilization of energy dissipation on a quantum neural network. Preliminary results which have been shown by numerical simulations indicate an availability of the energy dissipation for the quantum neural network.
UR - http://www.scopus.com/inward/record.url?scp=54049097574&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=54049097574&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69162-4_76
DO - 10.1007/978-3-540-69162-4_76
M3 - Conference contribution
AN - SCOPUS:54049097574
SN - 3540691596
SN - 9783540691594
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 730
EP - 737
BT - Neural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
T2 - 14th International Conference on Neural Information Processing, ICONIP 2007
Y2 - 13 November 2007 through 16 November 2007
ER -