Information Technology Reference
In-Depth Information
Chapter 10
The Phase of the “1”
Introduction
Electrical variables can carry a great deal of information, including waveform
shapes, durations, amplitudes, and phases. Previous chapters have mostly ignored
the phase variable for the waveform generated by a single recursive neuron. This
chapter introduces an auxiliary recursive neuron to operate at frequency f
1
, which is
the frequency for a logic 1. The purpose of the auxiliary recursive neuron operating
at f
1
is to register a relative delay termed Delay
β
, measured in microseconds, which
gives a phase angle
β
measured in degrees [
1
].
comes into play mainly when simulated qubits have been initialized to
have equal probabilities for false and true, although other situations are possible.
Applications considered below are as follows: (1)
Delay
β
works to identify an unknown
binary function of a single input, as held by a simulated qubit for which direct
observation is impractical. (2)
β
works to identify an unknown binary function of
many inputs, as held by many simulated qubits for which direct observation is
impractical. Other applications mentioned are as follows: (3) this chapter discusses
a satisfiability problem in which it is desired to identify inputs that satisfy a given
(known) Boolean multivibrator function of many simulated qubits, using a mini-
mum number of observations.
Finally, (4) this chapter investigates a way to use recursive neurons to increase
the density of stored information in long-term memory. This may be done by
packing data into a state vector based on simulated qubits, each of which is either
false with 100 % probability, true with 100 % probability, or both true and false
with equal 50 % probabilities.
Biological applications, if any, are not the focus of this chapter. Mainly this
chapter aids in understanding certain algorithms for simulated qubits, and develops
a pantheon of noteworthy operations that simulated qubits might accomplish.
β
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