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Bayesian Modeling of Quantum-Dot Cellular Automata Circuits

S. Bhanja and S.N. Srivastava
University of South Florida, US

quantum-dot cellular automata, nano-device modeling

The goal of this work is to develop a fast, Bayesian Probabilistic Computing model [1], [2] that exploits the induced causality of clocking to arrive at a model with the minimum possible complexity. The probabilities directly model the quantum-mechanical steady-state probabilities (density matrix) or equivalently, the cell polarizations. The attractive feature of this model is that not only does it model the strong dependencies among the cells, but it can be used to compute the steady state cell polarizations, without iterations or the need for temporal simulation of quantum mechanical equations. The impact of our proposed modeling is that it is based on density matrix-based quantum modeling, takes into account dependency patterns induced by clocking, and is noniterative. It allows for quick estimation and comparison of quantum-mechanical quantities for a QCA circuit, such as QCA-state occupancy probabilities or polarizations at any cell, thus enable one to quickly compare, contrast, and fine tune clocked QCA circuit designs, before performing costly full quantum-mechanical simulation of the temporal dynamics.

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