Neuromorphic quantum computation with energy dissipation

Journal Article · Tue Nov 15 04:00:00 EST 2005 · Physical Review. A

DOI:https://doi.org/10.1103/PHYSREVA.72.0· OSTI ID:20786480

Kinjo, Mitsunaga; Sato, Shigeo; Nakamiya, Yuuki; Nakajima, Koji ^[1]

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1. Laboratory for Brainware Systems, Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, 980-8577, 2-1-1 Katahira, Aoba-ku, Sendai (Japan)

Real parallel computing with a quantum computer attracts vast interest due to its extreme high potential. We propose a neuromorphic quantum computation algorithm based on an adiabatic Hamiltonian evolution with energy dissipation. This algorithm can be applied to problems if a cost function can be expressed in a quadratic form. This requirement results from the fact that our Hamiltonian is designed by following a method similar to an artificial neural network (ANN). The state of an ANN is often trapped at local minima, and the network outputs an error. Since the state of a quantum system with the proposed algorithm is always in the ground state according to the adiabatic theorem, it is not necessary to be concerned that the quantum state is trapped at local minima. However, there is no guarantee that a quantum algorithm based on an adiabatic Hamiltonian evolution with degeneration or level crossing is successfully executed. We show successful numerical simulation results with the proposed algorithm by introducing energy dissipation to keep the quantum state staying in the ground state, and then we show an application to the n-queen problem, which is one of the combinatorial optimization problems.

• OSTI ID: 20786480
• Journal Information: Physical Review. A, Journal Name: Physical Review. A Journal Issue: 5 Vol. 72; ISSN 1050-2947; ISSN PLRAAN
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
74 ATOMIC AND MOLECULAR PHYSICS
ALGORITHMS
COMPUTERIZED SIMULATION
ENERGY LOSSES
ERRORS
EVOLUTION
GROUND STATES
HAMILTONIANS
NEURAL NETWORKS
OPTIMIZATION
POTENTIALS
QUANTUM COMPUTERS
TRAPPING