Arithmetic Using Simulated Qubits

  • John Robert Burger
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 6)


This chapter reviews reversible computing using simulated qubits working as controlled toggle registers, and develops a plan for the generation of sums and carries for the addition of binary values as required to compute image priority. The wiring diagram method of reversible programming is demonstrated below, and this is used to explain reversible N-bit addition in terms of recursive neurons. Reversible adders using controlled toggles are controlled by code taken from long-term memory in order to calculate a numerical priority for each return from memory. Examples are given. Although not central to the theme of this book, priority calculations are likely to be efficient with energy since such operations are logically reversible and may approach physical reversibility as well.

Next, the highest priority value must be chosen. Priority comparison is explained using simulated qubits working as controlled toggles. This method involves subtraction of given priority values and testing the borrow-out to determine which priority value is larger. The 2’s complements method of subtraction is presented that can employ the reversible adders presented earlier. A complex of such operations results in the tagging of each returned image with a priority value. The highest priority image is arranged to be multiplexed into conscious short-term memory.

The described calculations use ordinary neurons working as toggles, and are expected to be massively parallel, taking place in real time. The underlying method is very powerful and indeed gifted mental calculators and savants may unknowingly employ similar neural processing in order to demonstrate their amazing brainpower.


Weighting Factor Logical Reversibility Toggle Switch Toffoli Gate Reversible Addition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • John Robert Burger
    • 1
  1. 1.VenetaUSA

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