The 2 Characteristics of a Quantum System, which are helpful in designing better algorithms for Quantum Systems are:
Superposition:
Superposition or Linear Combination of states helps perform parallel computations in a Single Circuit (In short, it speeds up the Execution).
Interference:
Helps us to gain infromation about a function $f(x)$ wrt various input values of $x$.
$\therefore$ it deduces certain global properties of the given function.
Helps create a superposition of states in the form of COMPUTATIONAL BASIS.
Further, $H(H\ket{\psi}) = \ket{\psi}$, for any state $\ket{\psi} = a\ket{0} + b\ket{1}$.
Hadmard Transform : $n$ Hadamard gates acting in parallel on $n$ Qubits creating a Superposition Phenomenon.
here, $x$ is represented in binary (one can clearly notice that the superposition has generated all possible n-qubit states with equal probability):
$(H\ket{0})^{\otimes n} = \left (\large \frac{\ket{0} + \ket{1}}{\sqrt{2}}\normalsize \right )^{\otimes n} = \large \frac{1}{\sqrt{2^{n}}} \normalsize \sum_{x=0}^{2^n - 1}\ket{x}$
Let’s now see , the affect of applying the Hadamard gate to each qubit of a general register of n-qubits:
Given by
$R_k = \left[\begin{matrix}1 & 0\\0 & e^{2\pi i/2^k}\end{matrix}\right]$ where, $k = \{0, 1, 2, \dots\}$
$R_3 = T$ (or $\pi/8$ gate)
Serial operations are given by product of operators:

Parallel operations are given by tensor product of operators to be applied in parallel:

Addition of Probability Amplitudes.
For example, consider the initial state:
$\ket{\psi} = \large \frac{\ket{0} + \ket{1}}{\sqrt{2}}$
it’s evident that the probability of measuring either $\ket{0}$ or $\ket{1}$ is (individually) = $\frac{1}{2}$.
However, if we apply the Hadamard Gate to this state, the resulting state will become:
$H(\ket{\psi}) = \large\frac{1}{\sqrt{2}}\left[ \frac{\ket{0} + \ket{1}}{\sqrt{2}} + \large \frac{\ket{0} - \ket{1}}{\sqrt{2}}\right]\normalsize = \ket{0}$
We notice a:
Problem Statement:
To determine whether a given boolean function $f(x)$ is constant or balanced. A function is said to be:
Constant, if $f(x) = 0$ or $f(x) = 1$ for all inputs. Example:
$f(x_0) = 0$
Balanced, if $f(x) = 0$ for half of the inputs and $f(x) = 1$ for the other half. Example:
$f(x_0, x_1, x_2) = x_0 \oplus x_1 \oplus x_2$
THE CLASSICAL COMPUTING APPROACH