1. Introduction
In quantum physics and chemistry, a quantum number represents a conserved quantity in the dynamics of a quantum system. Quantum numbers often specifically describe the energy levels of electrons in an atom, but other possibilities include the spin, angular momentum, and more. Since quantum numbers are defined in various ways depending on the subject, the quantum numbers with which we deal here are the following.
The quantum number (
q-number) introduced by Jackson [
1,
2] around 1900 is
for any positive integer
with
. Here, we note that
. In particular, for
,
is called a
q-integer.
The quantum Gaussian binomial coefficient is defined by
where
m and
r are non-negative integers; see [
2,
3,
4]. Note that
and
.
Many contributors have developed new theories related to quantum numbers in differential equations, integration, discrete distributions, series, etc.; see [
5,
6,
7,
8,
9]. Mathematicians working in this area have studied polynomial families such as those of Bernoulli, Euler, and Genocchi by using quantum numbers; see [
10,
11]. The authors of [
4] introduced
q-sigmoid (QS) polynomials while building on [
12].
Definition 1 ([
4])
. Let . QS numbers and polynomials are defined asrespectively. We note that
. In addition, it can be seen that QS numbers lead to a
q-sigmoid function; see [
13,
14]. The sigmoid function plays an important role as an activation function in deep learning, and it is currently the subject of active study; see [
15,
16]. For instance, Narayan [
17] used generalized nonlinear and differential sigmoid activation functions in a multilayer perceptron (MLP) network to demonstrate improved functionality, and they analyzed a well-known classification problem. Mulindwa and Du [
18] proposed the
n-sigmoid. Based on this, they introduced the results of further improving a network by introducing a new SE block, and they improved the learning and generalization abilities of two-dimensional and three-dimensional neural networks by reducing the gradient loss problem of the proposed function. There is an ongoing research effort to combine the sigmoid function and quantum numbers, and many new approaches are being explored; see [
19,
20].
Benaoum [
21] defined and studied the generalized
-Newton binomial formula of Sch
tzenberger’s formula. In [
22],
erm
k and Nechv
tal discussed
-integrals and derivatives involving the
h parameter in quantum numbers. They also defined Nabla
-fractional integrals and Delta
-fractional integrals based on
-calculus and presented their basic properties by introducing related derivatives. A two-parameter time scale
was introduced as follows:
Definition 2 ([
22,
23])
. Let be any function. Thus, the delta -derivative of f is defined by Definition 3 ([
21,
23])
. The generalized quantum binomial is defined bywhere . Definition 4 ([
10,
11,
23])
. The -exponential function is defined by Definition 5 ([
24,
25])
. Let be a fixed point. The Julia sequence is then We also know the Julia set for a complex plane, i.e., , where .
Based on the above concepts, the main goal of this study is to construct the most general DQS polynomial that can express both degenerate polynomials and polynomials combined with quantum numbers. Then, we obtain various forms of higher-order difference equations whose solutions are general DQS polynomials and show the properties of these equations. Furthermore, we explore their dynamic behavior by identifying the specific form of these polynomials. We use Newtonian methods to visualize Newtonian fractals and construct Julia sets to determine whether dynamic function systems appear in DQS polynomials.
This study’s structure is as follows. In
Section 2, we find different types of difference equations that have DQS polynomials as solutions. We also look for the symmetric properties of these difference equations.
Section 3 examines the results of changing the value of
q by selecting quartic polynomials from among the broader class of DQS polynomials. We use the Newton method to check for self-similarity and find several figures in the Julia set.
2. Several Difference Equations That are Related to DQS Polynomials
In this section, we define DQS polynomials and examine DQS numbers by using these polynomials. By using the -derivative, we also derive several difference equations that are related to DQS polynomials. We discuss some relations among DQS polynomials, QS polynomials, and degenerated sigmoid (DS) polynomials.
Definition 6. If h is a non-negative integer and , the DQS polynomials are defined as follows: For
, we note that
and we call
the DQS numbers.
By modifying the conditions of
q and
h in Definition 6, we can see that there are several types of sigmoid numbers and polynomials. Let
in the DQS polynomials. Then, we have the following sigmoid numbers
and polynomials
:
Putting
in the DQS polynomials, we find the following QS numbers
and polynomials
:
Furthermore, for
in the DQS polynomials, we define the DS numbers
and polynomials
as follows:
where
.
Theorem 1. For and , we obtain Proof. By using the DQS numbers and Cauchy product in Definition 6, we find that
By comparing the left and right sides of the equation above, one can find the relationship between the DQS number and the polynomial, as shown in (1).
By applying Definition 2 in Equation (
1), we obtain the following equation:
Applying Equation (
1) to the above equation gives us the desired result. □
Corollary 1. Consider that ρ is a non-negative integer in Theorem 1. Then, the following equation holds: Corollary 2. We obtain the following results when we use the following conditions in Theorem 1:
- (i)
Setting , we havewhere is the h-derivative, and represents the DS polynomials. - (ii)
Setting , we havewhere is the q-derivative, and represents the QS polynomials.
Theorem 2. The -difference equationhas a solution with DQS polynomials. Proof. Consider that
in the definition of DQS polynomials. Then, we have
The left and right sides of Equation (
2) can be changed as follows:
and
From the above equations, we can obtain
Applying Corollary 1 to Equation (
3), we obtain
which is the desired result. □
Corollary 3. Setting in Theorem 2, it holds thatwhere is the h-derivative, and represents the DS polynomials. Corollary 4. Let in Theorem 2. Then, it holds thatwhere is the q-derivative, and represents the QS polynomials. Theorem 3. DQS polynomials are a solution of a -difference equation of higher order: Proof. Definition 6 can be considered as follows:
By using the definitions of DQS numbers and polynomials in the above equation and the Cauchy product, we find the following relationship:
Comparing the coefficients on both sides, the above equation is changed into Equation (
4) as follows:
Using
to replace
in Equation (
4), we obtain
By expanding the equation above, we finish the proof of Theorem 3. □
Corollary 5. If we set in Theorem 3, we can obtain the difference equation related to the QS polynomials as follows: Corollary 6. If we use in Theorem 3, we can obtain the difference equation related to the DS polynomials as follows: Theorem 4. DQS polynomials are solutions of the following higher-order -difference equation: Proof. To obtain Theorem 4, we check one of the properties of
as follows:
Consider Equation (
5) in
from Definition 6. Then, we obtain
From
, we have the following relation:
From the above equation, we find that
Substituting
for
in Corollary 1, we note that
By applying Equation (
7) in Equation (
6), we have
which has the required result. □
Corollary 7. Setting in Theorem 4, we obtain the difference equation of the QS polynomials with the q-derivative . Theorem 5. Let with . Then, we find a basic symmetry property of the -difference equation: Proof. Considering
, we suppose the form
A as follows:
From form
A, we can derive
and
Comparing the coefficients of both sides in Equations (
8) and (
9), we find
From Corollary 1, we can note that
Replacing Equation (
10) with Equation (
11), we have
By using Equation (
12), we complete the proof of Theorem 5. □
Corollary 8. From Theorem 5, we hold the following:
(i) For , this satisfies the following: (ii) For , this satisfies the following: 3. Fractal Phenomenon and Several Figures for Quartic DQS Polynomials
In this section, we examine the phenomena of the dynamics of quartic DQS polynomials in a space with a complex structure. We identify properties for DQS polynomials related to them by using the Newton method and by finding the Julia set.
By using Equation (
3), the DQS polynomial
can be found:
Table 1 is a DQS polynomial
that is obtained as the value of
q changes when
h is fixed to 1. We can guess that the DQS polynomials that appear when
is considered are similar to the DS polynomials, and as the value of
q becomes smaller, the properties of both the DS polynomials and the QS polynomials appear.
From now on, we use the Newton method for the polynomials in
Table 1; see [
24,
26].
- (1)
Number of iterations: 50;
- (2)
;
- (3)
Range of for : , ;
- (4)
We obtained
Figure 1 through the process of Steps 1 and 2 by using a computer. The positions of each approximate root appearing in
Figure 1 are as follows:
- (a)
Approximate roots of : .
- (b)
Approximate roots of : .
- (c)
Approximate roots of : .
Panels (a,b) in
Figure 1 show that complex numbers have approximate roots of
and
, and they are displayed in yellow and ultramarine blue, respectively. In addition, red and blue colors indicate real numbers as approximate roots.
Figure 1c shows that all approximate roots are real numbers:
is red,
is ultramarine blue,
is yellow, and
is blue. In panels (a–c) of
Figure 1, the areas indicated by colors mean that they approach the approximate root corresponding to each color. For example, the red area in
Figure 1a has an approximated fixed point of
, and if the initial value is near the approximated fixed point, it becomes an element of a basin of attraction. Even if a point in the complex plane is far from the approximated fixed point, if the initial value is in the red area, the point will converge to the approximated fixed point after infinite time passes. In
Figure 1, whenever each color is about to be combined, a patch of a different color appears in between. This behavior repeats on an infinitely small scale, producing a fractal. This figure shows a Newtonian fractal, which is the result of using Newton’s method, and the basin of attraction is even more interesting; see [
26,
27].
Figure 2 depicts a fractal from the Julia set of
. Here, we assume that the function is iterated 128 times and choose a convergence radius of 2. The image centers in
Figure 2a,b are
. In
Figure 2a, the value of the Julia offset is given as
, and in
Figure 2b, the value of the Julia offset is
.
Figure 2a,b does not have a fixed point, and periodic points cannot be found. We can consider symmetric properties by looking at
Figure 2a,b. In
Figure 2a,b, it can be seen that the range of the
x-axis is
, and the range of the
y-axis is
. In addition, it can be seen that the area that appears after 128 iterations is a dot in the red part.
Figure 2c shows the shape that appears when the offset is given as
. A Julia set appears in the range of
,
, with an image center of
. The line that appears here shows the trace of
, which repeats up to 128 times as a three-period point.
In
, we set the number of iterations to 32 and fix the range of convergence to 4. We also limit the range to
and
. Then, we find the Julia set, as shown in
Figure 3. Here, the offsets of (a–c) are
,
, and
, respectively. In
Figure 3a, there are two fixed points, and they appear as green dots. This shows that
arrives at the fixed point in fewer than 10 iterations. If we change the offset of
Figure 3a to the offset of
Figure 3b, we find that
Figure 3b is in the same range as
Figure 3a. Here, it can be seen that there are three fixed points, and an attracting point can also be found. It can be seen that the point
in
Figure 3b arrives at a fixed point. In
Figure 3c, we can see that there is only one fixed point. However, after 64 iterations in
Figure 3c, no fixed point exists.
For the condition in
Figure 4,
was iterated 32 times, and the convergence radius was set to 2.
Figure 4a shows the continent-shaped Julia set, and the fixed point is represented by the green dot. The offset in
Figure 4a is
, and when the range of the offset is
,
, the shape of the continent is almost similar to that in figure in (a).
Figure 4b shows a Julia set filled with basilica, and the actual Julia set is defined by the boundary of the blue area. The offset in
Figure 4b is
, and the offset in
Figure 4c is
.
Figure 4c is renormalized once based on
Figure 4b, and it includes the small basilica Julia set. This renormalization can be repeated continuously to obtain an infinite basilica. In other words, since the renormalization of the basilica becomes itself, the renormalization can be repeated over and over again. The offset in
Figure 4d is
, and this figure is called the Feigenbaum Julia set. This figure can be considered a Julia set using Feigenbaum polynomials that can be renormalized infinite times.