Iris Data Classification Using Quantum Neural Networks
- Conference date: 6-12 March 2006
- Location: Kanpur (India)
Quantum computing is a novel paradigm that promises to be the future of computing. The performance of quantum algorithms has proved to be stunning. ANN within the context of classical computation has been used for approximation and classification tasks with some success. This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. It synergizes the unique properties of quantum bits or qubits with the various techniques in vogue in neural networks. An example application of Fisher’s Iris data set, a benchmark classification problem has also been presented. The results obtained amply demonstrate the classification capabilities of the quantum neuron and give an idea of their promising capabilities.
- Artificial neural networks
- Physics demonstrations
- Quantum algorithms
- Quantum computing
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Y. K. Semertzidis, M. Aoki, M. Auzinsh, V. Balakin, A. Bazhan, G. W. Bennett, R. M. Carey, P. Cushman, P. T. Debevec, A. Dudnikov, F. J. M. Farley, D. W. Hertzog, M. Iwasaki, K. Jungmann, D. Kawall, B. Khazin, I. B. Khriplovich, B. Kirk, Y. Kuno, D. M. Lazarus, L. B. Leipuner, V. Logashenko, K. R. Lynch, W. J. Marciano, R. McNabb, W. Meng, J. P. Miller, W. M. Morse, C. J. G. Onderwater, Y. F. Orlov, C. S. Ozben, R. Prigl, S. Rescia, B. L. Roberts, N. Shafer‐Ray, A. Silenko, E. J. Stephenson, K. Yoshimura and EDM Collaboration
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