Simple translation from the QM formalism to HNeT is as follows:



is the stimulus state for pattern n where phase angle represents semantic information (intensity, pressure, temperature, etc,); amplitude is probability.



c_{n} 

is the associated response vector, and





stores (superimposes) associative memory across all patterns that have been learned, representing memory contained within one neuron cell [X].



Holographic neural processing, and more recent "Quantum Neural Network" and Quantum Mind theories published by academia are based upon this linear process (1) in learning and (2) in recall; again applying phase coherence/decoherence in the superposition of information, and achieving parallel computation during associative recall. Equation (2) forming the operation of associative recall is often referred to in QM jargon as collapse of the wave function.
Entanglement within Quantum Information Processing (QIP) is analogous to the combinatorics stage of the HNeT process. We apply analog range values throughout the computation (phase information, amplitude probability), and are not restricted to measurement of binary (qubit) states. These aspects, combined with neural plasticity in optimization of higher order combinatorics and reinforcement learning, provide unprecedented capability for learning and generalization within the system.
