Re-constructing memory using quantized electronic music and a ‘Toridion byte’ quantum algorithm: Creating images using zero logic quantum probabilistic neural networks (ZLQNN)

Quantum theory applied to data analytics using a quantum computer has become the leading research endeavour to find a way to store and retrieve data using the nano-sized world of molecular structures. Much of the theorization that is applied to quantum computer development relies on a conceptual framework largely based on metaphors to understand the behaviour of sub-atomic elements within a quantum field. One aspect of the quantum field is the entanglement of elements, whereby behaviours of two distinct elements respond to change independent of their location. The Toridion quantum algorithm was used to scatter pre-recorded sound into frequency amplitudes within a simulated quantum computer environment. The sounds were composed by using quantum cognitive meta models for the creation of electronic music compositions. The Toridion Encoder creates highly compressed ‘glyphs’ of the sounds whilst simultaneously creating a probabilistic quantum neural network within the cyclic mental workspace of the computer. This article will explain how using a quantum compositional framework in composing electronic music orchestrations can aid in retrieving lost memories of either images or verbal expressions. The implications for exploring a quantum language (exo-language) formed by self-organizing principles in the quantum field and interpreted using the Toridion quantum algorithm’s search function will also be discussed.