Dr. Elizabeth Behrman

Professor Behrman's research interests and publications are broad, ranging from chemical kinetics and reaction pathways to ceramic superconductors to nuclear waste vitrification. She was the first to predict the stability of inorganic buckyballs and buckytubes, and among the first to design and computationally test models for quantum neural networks. Her major focus for several decades has been theoretical quantum computing, quantum information, and quantum control, particularly quantum machine learning and quantum AI. In this work, she is collaborating with colleagues in Mathematics, Electrical and Computer Engineering, Aerospace Engineering, and Chemistry, both at WSU and other universities. See also: http://quantumbusiness.org/focus-quantum-computers-supercharge-artificial-intelligence/ and https://www.youtube.com/watch?v=cEiVYZYrrEc 

1. N.H. Nguyen, E.C. Behrman, M.A. Moustafa, and J.E. Steck, “Benchmarking neural networks for quantum computation,” submitted to Quantum Information and Computation (2018). arXiv:1807.03253

2. E.C. Behrman, J.E. Steck, and M.A. Moustafa, “Learning quantum annealing,” Quantum Information and Computation 17, 0469-0487 (2017). arXiv:1603.01752

3. E.C. Behrman, N.H. Nguyen, J.E. Steck, and M. McCann, “Quantum neural computation of entanglement is robust to noise and decoherence,” in Quantum Inspired Computational Intelligence: Research and Applications, S. Bhattacharyya, ed. (Morgan Kaufmann, Elsevier, 2016.)

4. E.C. Behrman, R.E.F. Bonde, J.E. Steck, and J.F. Behrman, “On the correction of anomalous phase oscillation in entanglement witnesses using quantum neural networks," IEEE-Transactions on Neural Networks and Learning Systems 25, 1696-1703 (2014).

5. E.C. Behrman and J.E. Steck, “Multiqubit entanglement of a general input state,” Quantum Information and Computation 13, 36-53 (2013).

6. M.J. Rethinam, A.K. Javali, A.E. Hart, E.C. Behrman, and J.E. Steck, “A genetic algorithm for finding pulse sequences for nmr quantum computing,” Paritantra – Journal of Systems Science and Engineering 20, 32-42 (2011).

7. E.C. Behrman, J.E. Steck, P. Kumar, and K.A. Walsh, “Quantum algorithm design using dynamic learning,” Quantum Information and Computation 8, 12-29 (2008)

8. R. Allauddin, K. Gaddam, S. Boehmer, E.C. Behrman, and J.E. Steck, “Quantum simultaneous recurrent networks for content addressable memory,” in Quantum-Inspired Intelligent Systems, N. Nedjah, L. dos Santos Coelho, and L. de Macedo Mourelle, eds. (Springer Verlag, 2008).

9. P.K. Gagnebin, S.R. Skinner, E.C. Behrman, and J.E. Steck, “Quantum state transfer with untunable couplings,” Physical Review A 75, 022310 (2007); selected for the February 26, 2007 issue of Virtual Journal of Nanoscale Science and Technology, available at http://www.vjnano.org; for the February 2007 issue of Virtual Journal of Quantum Information, available at http://www.vjquantuminfo.org.

10. E.C. Behrman, K. Gaddam, J.E. Steck, and S.R. Skinner, “Microtubules as a quantum Hopfield network,” in The Emerging Physics of Consciousness, J.A. Tuszynski, ed. (Springer Verlag, 2006.)

11. P.K. Gagnebin, S. Skinner, E.C. Behrman, J.E. Steck, Z. Zhou, and S. Han, “Quantum gates using a pulsed bias scheme,” Physical Review A 72, 042311 (2005); selected for the October 17, 2005 issue of Virtual Journal of Nanoscale Science and Technology, available at http://www.vjnano.org; for the October 2005 issue of Virtual Journal of Quantum Information, available at http://www.vjquantuminfo.org; and for the October 15, 2005 issue of Virtual Journal of Applications of Superconductivity, available at http://www.vjsuper.org.