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Neuromorphic Adiabatic Quantum Computation

Neuromorphic Adiabatic Quantum Computation

Shigeo Sato, Mitsunaga Kinjo
Copyright: © 2009 |Pages: 24
ISBN13: 9781605662145|ISBN10: 1605662143|ISBN13 Softcover: 9781616925628|EISBN13: 9781605662152
DOI: 10.4018/978-1-60566-214-5.ch014
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MLA

Sato, Shigeo, and Mitsunaga Kinjo. "Neuromorphic Adiabatic Quantum Computation." Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, IGI Global, 2009, pp. 352-375. https://doi.org/10.4018/978-1-60566-214-5.ch014

APA

Sato, S. & Kinjo, M. (2009). Neuromorphic Adiabatic Quantum Computation. In T. Nitta (Ed.), Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters (pp. 352-375). IGI Global. https://doi.org/10.4018/978-1-60566-214-5.ch014

Chicago

Sato, Shigeo, and Mitsunaga Kinjo. "Neuromorphic Adiabatic Quantum Computation." In Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, 352-375. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-214-5.ch014

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Abstract

The advantage of quantum mechanical dynamics in information processing has attracted much interest, and dedicated studies on quantum computation algorithms indicate that a quantum computer has remarkable computational power in certain tasks. Quantum properties such as quantum superposition and quantum tunneling are worth studying because they may overcome the weakness of gradient descent method in classical neural networks. Also, the technique established for neural networks can be useful for developing a quantum algorithm. In this chapter, first the authors show the effectiveness of incorporating quantum dynamics and then propose neuromorphic adiabatic quantum computation algorithm based on the adiabatic change of Hamiltonian. The proposed method can be viewed as one of complex-valued neural networks because a qubit operates like a neuron. Next, the performance of the proposed algorithm is studied by applying it to a combinatorial optimization problem. Finally, they discuss the learning ability and hardware implementation.

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