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Flexibility in Grover's Algorithm?

  1. Sep 26, 2013 #1
    Grover's Algorithm is a QM process for finding a high-scoring element in a large data base.

    My specific curiosity if of a biological system that would generate a superpositioning of data that represented potential intentions, to estimate the consequences of these potential intentions based on previous experience, then to rate these consequences according to the likely "goodness" to the biological system, and then to pick out this "best of intentions" using Grover's Algorithm.

    Although I do not doubt that some QM information processing can be done in the "wet" environment of a biological system (http://phys.org/news184423418.html), the "averaging" step in the algorithm impresses me as potentially challenging for such wetness - especially when it needs to be repeated.

    So here are my questions:

    If the Grover Algorithm is implemented in a looser fashion, would the results fall apart completely, or could you get a "good" intention selected - even if it may seldom be the "best".

    The algae photosynthesis demonstrated that biological molecules can be used for processing superpositioned states to the advantage of the organism in a wet environment. Making use of such devices in a brain for the purpose I described above would require superpositioning that stretched across many such molecules and would involve many qubits (or QM analog equivalents) involved in a dingle superpositioning. Given the technology for creating a brain at all, are there any theoretical or practical restrains to keep this from being implemented?


    Scott Bowden
  2. jcsd
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