Now showing items 1-9 of 9

    • Biologically motivated quantum neural networks 

      Steck, James E.; Behrman, Elizabeth C. (IEEE, 2017)
      This paper presents one step toward creating the building blocks for machine intelligence that is inspired by its biological equivalent. The authors' quantum learning methods (deep quantum learning) are applied to quantum ...
    • Chapter 1 -- Quantum neural computation of entanglement is robust to noise and decoherence 

      Behrman, Elizabeth C.; Nguyen, Nam H.; Steck, James E.; McCann, M. (Elsevier, 2017)
      Measurement and witnesses of entanglement remain an important issue in quantum computing. Most witnesses will work for only a very restricted class of states, while measurements commonly require lengthy procedures. Quantum ...
    • Experimental pairwise entanglement estimation for an N-qubit system: a machine learning approach for programming quantum hardware 

      Thompson, Nathan L.; Nguyen, Nam H.; Behrman, Elizabeth C.; Steck, James E. (Springer, 2020-11-03)
      Designing and implementing algorithms for medium- and large-scale quantum computers is not easy. In the previous work, we have suggested, and developed, the idea of using machine learning techniques to train a quantum ...
    • Multiqubit entanglement of a general input state 

      Behrman, Elizabeth C.; Steck, James E. (Rinton Press, Inc., 2013-01)
      Measurement of entanglement remains an important problem for quantum information. We present the design and simulation of an experimental method for an entanglement indicator for a general multiqubit state. The system can ...
    • On the correction of anomalous phase oscillation in entanglement witnesses using quantum neural networks 

      Behrman, Elizabeth C.; Bonde, Richard E. F.; Steck, James E.; Behrman, Joanna F. (IEEE, 2013-10-01)
      Entanglement of a quantum system depends upon the relative phase in complicated ways, which no single measurement can reflect. Because of this, "entanglement witnesses'' (measures that estimate entanglement) are necessarily ...
    • Quantum machine learning: Preface 

      Bhattacharyya, Siddhartha; Pan, Indrajit; Mani, Ashish; De, Sourav; Behrman, Elizabeth C.; Chakrabarti, Susanta (De Gruyter, 2020-06-08)
      Imparting intelligence to the machines has always been a challenging thoroughfare. Over the years, several intelligent tools have been invented or proposed to deal with the uncertainties encountered by human beings with ...
    • A quantum neural network computes its own relative phase 

      Behrman, Elizabeth C.; Steck, James E. (IEEE, 2013-04-17)
      Complete characterization of the state of a quantum system made up of subsystems requires determination of relative phase, because of interference effects between the subsystems. For a system of qubits used as a quantum ...
    • Qutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentation 

      Konar, Debanjan; Bhattacharyya, Siddhartha; Panigrahi, Bijaya K.; Behrman, Elizabeth C. (Institute of Electrical and Electronics Engineers, 2021-05-13)
      Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, ...
    • Reinforcement and backpropagation training for an optical neural network using self-lensing effects 

      Cruz-Cabrera, A.A.; Yang, Mingtao; Cui, Guogi; Behrman, Elizabeth C.; Steck, James E.; Skinner, Steven R. (IEEE, 2000-11)
      The optical bench training of an optical feedforward neural network, developed by the authors, is presented. The network uses an optical nonlinear material for neuron processing and a trainable applied optical pattern as ...