Analogical Modeling and Quantum Computing

Abstract

This paper serves as a bridge between quantum computing and analogical modeling (a general theory for predicting categories of behavior in varying contexts). Since its formulation in the early 1980s, analogical modeling has been successfully applied to a variety of problems in language. Several striking similarities between quantum mechanics and analogical modeling have recently been noted: (1) traditional statistics can be derived from a non-statistical basis by assuming data occurrences are accessed through a spin-up state (given two equally probable quantum states, spin-up and spin-down); (2) the probability of predicting a particular outcome is determined by the squaring of an underlying linear measure and is the result of decoherence (which occurs when a quantum system is observed); and (3) a natural measure of certainty (called the agreement) is based on one chance of guessing the right outcome and corresponds to the integrated squaring of Schroedinger's wave equation. Analogical modeling considers all possible combiantions of a given context of n variables, which is classical terms leads to an exponential explosion on the order of 2**n. This paper proposes a quantum computational solution to this exponentiality by applying a cycle of reversible quantum operators to all 2**n possibilities, thus reducing the time and space of analogical modeling to a polynomial order.