论文信息 - A Study on Learning with a Quantum Neural Network

A Study on Learning with a Quantum Neural Network

A quantum neural network based on the adiabatic quantum computation is one of candidates to overcome the difficulty for developing a quantum computation algorithm. In this paper, we propose a new learning method for a quantum neural network inspired by Hebb learning. Preliminary but successful results by numerical simulations have been shown. The results indicate that a quantum learning like Hebb rule can be implemented.

Shigeo Sato | Koji Nakajima | Mitsunaga Kinjo

[1] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[2] Shigeo Sato,et al. Neuromorphic quantum computation with energy dissipation , 2005 .

[3] A. Messiah. Quantum Mechanics , 1961 .

[4] Peter W. Shor,et al. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1995, SIAM Rev..

[5] John J. Hopfield,et al. Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

[6] Peter W. Shor. Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer , 1999 .

[7] Shigeo Sato,et al. An Approach for Quantum Computing using Adiabatic Evolution Algorithm , 2003 .

[8] E. Farhi,et al. A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.

[9] Erkki Oja,et al. Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing , 2003 .

[10] Thierry Paul,et al. Quantum computation and quantum information , 2007, Mathematical Structures in Computer Science.

[11] Lov K. Grover. A fast quantum mechanical algorithm for database search , 1996, STOC '96.