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The above method is a variation on Deutsch's quantum algorithm [
2
]. For larger
numbers of simulated qubits additional global properties of multivibrator functions
become identifiable.
Symmetric and Antisymmetric Multivibrator Function Classification
Using n Simulated Qubits
Given n synchronized multivibrators, a multivibrator function operates on each
possible combination for which there is a probability. For example, for n
¼
2, there
could be a finite probability for each combination a
1
b
1
, a
1
b
2
, a
2
b
1
, a
2
b
2
.
The class of functions g
sa
(symmetric and antisymmetric functions) can be
identified by their patterns in truth tables [
3
]. Symmetric means that the entries
below the center of the truth table is a mirror image about the center of the half
above the center. Antisymmetric means that a bitwise complement of the second
half is a mirror image about the center of the first half.
A symmetric or antisymmetric function g
sa
of n binary variables with dimension
2
n
has the property of being symmetric or antisymmetric about the center of its
truth table and being symmetric or antisymmetric in each binary subdivision of 2
ni
entries of the table i
k
¼
1. This class of binary functions has
an even number of true entries in its truth table for n
¼
1, 2,
, n
1 for n
>
1 is a limiting case
for constant and non-constant binary functions of one bit as above; any binary
function with n
>
1; n
¼
¼
1 is either symmetric or antisymmetric. Examples for n
¼
2 are
provided in Tables
10.3
and
10.4
.
Assume that a multivibrator function in this class has been applied to the
probability space of n multivibrators but that the actual function is unknown. As
above, it is not permitted to place an oscilloscope probe directly into a recursive
neuron because it is assumed small, delicate, and easily upset. A procedure for
Table 10.3
g
sa
¼
x
1
x
2
,
Truth table
x
1
symmetric
x
2
g
as
0
0
0
0
1
1
1
0
1
1
1
0
Table 10.4
g
sa
¼
x
1
,
Truth table
x
1
antisymmetric
x
2
g
as
0
0
0
0
1
0
1
0
1
1
1
1
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