Publications

Journal Papers

1. Araujo, I. F., Park, D. K., Petruccione, F., & da Silva, A. J. (2021). A divide-and-conquer algorithm for quantum state preparation. Scientific Reports, 11(1), 1-12.
2. Gamboa, J. C. R., da Silva, A. J., Araujo, I. C., Albarracin, E., & Duran C. (2021). Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines. Sensors and Actuators B: Chemical, 327, 128921. Paper also available here.
3. Veras, T. M., De Araujo, I. C., Park, K. D., & Dasilva, A. J. (2020). Circuit-based quantum random access memory for classical data with continuous amplitudes. IEEE Transactions on Computers. Paper also available here
4. Sousa, R. S., dos Santos, P. G., Veras, T. M., de Oliveira, W. R., & da Silva, A. J. (2020). Parametric Probabilistic Quantum Memory. Neurocomputing. Paper also available here
5. Sousa, R. S., dos Santos, P. G., Veras, T. M., de Oliveira, W. R., & da Silva, A. J. (2020). Parametric Probabilistic Quantum Memory. Neurocomputing. Paper also available here
6. de Paula Neto, F. M., Ludermir, T. B., de Oliveira, W. R., & da Silva, A. J. (2019). Implementing Any Nonlinear Quantum Neuron. IEEE Transactions on Neural Networks and Learning Systems.
7. Gamboa, Juan C. Rodriguez, Adenilton J. da Silva, and Tiago AE Ferreira. "Electronic nose dataset for detection of wine spoilage thresholds." Data in brief 25 (2019): 104202.
8. Gamboa, J.C.R., da Silva, A.J., de Andrade Lima, L.L. and Ferreira, T.A., 2019. Wine quality rapid detection using a compact electronic nose system: Application focused on spoilage thresholds by acetic acid. LWT - Food Science and Technology, 108, pp.377-384. Paper also available here
9. de Paula Neto, F.M., da Silva, A.J., de Oliveira, W.R. and Ludermir, T.B., 2019. Quantum probabilistic associative memory architecture. Neurocomputing, 351, pp.101-110.
10. dos Santos, P.G., Sousa, R.S., Araujo, I.C. and da Silva, A.J., 2018. Quantum enhanced cross-validation for near-optimal neural networks architecture selection. International Journal of Quantum Information, 16(08), p.1840005. Paper also available here
11. de Paula Neto, F.M., de Oliveira, W.R., Ludermir, T.B. and da Silva, A.J., 2017. Chaos in a quantum neuron: An open system approach. Neurocomputing, 246, pp.3-11.
12. da Silva, A.J., Ludermir, T.B. and de Oliveira, W.R., 2016. Quantum perceptron over a field and neural network architecture selection in a quantum computer. Neural Networks, 76, pp.55-64. Paper also available here
13. da Silva, A.J., de Oliveira, W.R. and Ludermir, T.B., 2016. Weightless neural network parameters and architecture selection in a quantum computer. Neurocomputing, 183, pp.13-22. Paper also available here
14. da Silva, A.J. and de Oliveira, W.R., 2016. Comments on “quantum artificial neural networks with applications”. Information Sciences, 370, pp.120-122.
15. de Paula Neto, F.M., de Oliveira, W.R., da Silva, A.J. and Ludermir, T.B., 2016. Chaos in quantum weightless neuron node dynamics. Neurocomputing, 183, pp.23-38.
16. da Silva, A.J., de Oliveira, W.R. and Ludermir, T.B., 2015. Comments on “quantum MP neural network”. International Journal of Theoretical Physics, 54(6), pp.1878-1881.
17. de Lima, T.P., da Silva, A.J., Ludermir, T.B. and de Oliveira, W.R., 2014. An automatic methodology for construction of multi-classifier systems based on the combination of selection and fusion. Progress in Artificial Intelligence, 2(4), pp.205-215.
18. da Silva, A.J., De Oliveira, W.R. and Ludermir, T.B., 2012. Classical and superposed learning for quantum weightless neural networks. Neurocomputing, 75(1), pp.52-60.