Mathematics is full of weird stuff. One of them is the square root of an operator. So far, I have seen only two books that discuss the square root of an operator. They are listed in the references below.

Definition. An operator $R$ is called a square root of an operator $T$ is $R^2=T$.

Example. In quantum computing, $\sqrt{\mathrm{NOT}}$ gate is given by
$$\sqrt{\mathrm{NOT}}=\begin{bmatrix}
\frac{1+i}{2} & \frac{1-i}{2}\\
\frac{1-i}{2} & \frac{1+i}{2}
\end{bmatrix}$$
and
$$\sqrt{\mathrm{NOT}}\cdot\sqrt{\mathrm{NOT}}=\mathrm{NOT}=\begin{bmatrix}
0 & 1\\
1 & 0
\end{bmatrix}$$
As required by quantum mechanics, quantum gates are unitary matrices. There is no counterpart of $\sqrt{\mathrm{NOT}}$ gate in classical computing, so $\sqrt{\mathrm{NOT}}$ gate is a truly quantum gate. As far as I know, no one has come up with a physical implementation of $\sqrt{\mathrm{NOT}}$ gate yet.

Example. An operator does not necessarily have a square root. For example, define $T:\mathbb{C}^3\longrightarrow\mathbb{C}^3$ by
$$T(z_1,z_2,z_3)=(z_2,z_3,0)$$
Then one can easily show that $T$ is linear. Suppose that $T$ has a square root $R$. Let $\begin{bmatrix}
r & s & t\\
u & v & w\\
z & y & z
\end{bmatrix}$ be the matrix associated with $R$. Since the matrix associated with $T$ is $\begin{bmatrix}
0 & 1 & 0\\
0 & 0 & 1\\
0 & 0 & 0
\end{bmatrix}$, we have the equation
$$\begin{bmatrix}
r & s & t\\
u & v & w\\
z & y & z
\end{bmatrix}\cdot\begin{bmatrix}
r & s & t\\
u & v & w\\
z & y & z
\end{bmatrix}=\begin{bmatrix}
0 & 1 & 0\\
0 & 0 & 1\\
0 & 0 & 0
\end{bmatrix}$$
This is a system of 9 scalar equations. One can attempt to solve this equation using a CAS (Computer Algebra System), for example, Maple and see that the system does not have a solution, i.e. $R$ does not exist.

So, what kind of operators have square roots?

Theorem. Identity + Nilpotent has a square root.

Proof. Let $N$ be nilpotent. Then $N^m=0$ for some positive integer $m$. Consider the Taylor series for $\sqrt{1+x}$:
$$\sqrt{1+x}=1+a_1x+a_2x^2+a_3x^3+\cdots$$
Using the nilpotency of $N$, we may guess that $\sqrt{I+N}$ takes the form
$$I+a_1N+a_2N^2+\cdots+a_{m-1}N^{m-1}$$
Now,
\begin{align*} (I+a_1N+a_2N^2+\cdots+a_{m-1}N^{m-1})^2=I&+2a_1N+(2a_2+a_1^2)N^2\\&+(2a_3+2a_1a_2)N^3+\cdots\\ &+(2a_{m-1}+\cdots)N^{m-1}=I+N \end{align*}
and by comparing the coefficients
$a_1=\frac{1}{2}$, $2a_2+a_1^2=0$, so $a_2=-\frac{1}{8}$, $2a_3+2a_1a_2=0$, so $a_3=\frac{1}{16}$, and so and forth.

Theorem. Suppose that $T: V\longrightarrow V$ is invertible. Then $T$ has a square root.

Proof. Let $\lambda_1,\lambda_2,\cdots,\lambda_m$ be the distinct eigenvalues of $T$. Then for each $j$, there exists a nilpotent operator $N_j: G(\lambda_j,T)\longrightarrow G(\lambda_j,T)$ such that $T|{G(\lambda_j,T)}=\lambda_jI+N_j$. (See [1] , p 252, Theorem 8.21 for a proof.) Here, $G(\lambda_j,T)$ is the eigenspace of $T$ corresponding to the eigenvalue $\lambda_j$. Since $T$ is invertible, no $\lambda_j$ is equal to 0, so we can write $$T|{G(\lambda_j,T)}=\lambda_j\left(I+\frac{N}{\lambda_j}\right)$$
Since $\frac{N}{\lambda_j}$ is nipotent, $I+\frac{N}{\lambda_j}$ has a square root. Let $R_j$ be $\sqrt{\lambda_j}$ times the square root of $I+\frac{N}{\lambda_j}$. Any vector $v\in V$ can be written uniquely in the form
$$v=u_1+u_2+\cdots+u_m,$$
where each $u_j$ is in $G(\lambda_j,T)$. Using this decomposition, define an operator $R:V\longrightarrow V$ by
$$Rv=R_1u_1+R_2 u_2+\cdots+R_m u_m$$
Then $R^2u_j=R_j^2 u_j=T|{G(\lambda_j,T)}u_j=\lambda_ju_j$ and so \begin{align*} R^2v&=R_1^2u_1+R_2^2u_2+\cdots+R_m^2u_m\\ &= T|{G(\lambda_1,T)}u_1+T|{G(\lambda_2,T)}u_2+\cdots+T|{G(\lambda_m,T)}u_m=Tv
\end{align*}
Therefore, $R$ is a square root of $T$.

Example. The $\mathrm{NOT}$ gate is invertible. It is an inverse of itself.

The proof of the above theorem suggests that an operator $T$ has a square root if there is a spectral decomposition of $T$ with respect to the standard inner product of $\mathbb{C}^n$. A normal operator is such an operator. Recall that an operator $T: V\longrightarrow V$ is normal if $T^\ast T=TT^\ast$. The following theorem guarantees the existence of such a spectral decomposition of a normal operator.

Theorem. A matrix $T$ is normal if and only if there exists a diagonal matrix $\Lambda$ and an unitary matrix $U$ such that $T=U\Lambda U^\ast$.

Here, the diagonal entries of $\Lambda$ are the eigenvalues of $T$ and the columns of $U$ are the eigenvectors of $T$. The matching eigenvalues in $\Lambda$ come in the same order as the eigenvectors are ordered as columns of $U$.

Let $T: V\longrightarrow V$ be a normal operator and write it as its spectral decomposition, using Dirac’s braket notation,
$$T=\sum_j\lambda_j|v_j\rangle\langle v_j|$$
Define
$$f(T)=\sum_j f(\lambda_j)|v_j\rangle\langle v_j|$$
Using this definition, one can define, for example, the exponential, square root, and logarithmic operator of $T$.

Example. Let $T=\begin{bmatrix}
0 & 1\\
1 & 0
\end{bmatrix}$. Find the square root of $T$.

Solution. $T$ is hermitian (symmetric), so it is normal. The spectral decomposition of $T$ is given by
$$T=|v_1\rangle\langle v_1|-|v_2\rangle\langle v_2|,$$
where
$$|v_1\rangle=\frac{1}{\sqrt{2}}\begin{bmatrix}
1\\
1
\end{bmatrix}\ \mathrm{and}\ |v_2\rangle=\frac{1}{\sqrt{2}}\begin{bmatrix}
1\\
-1
\end{bmatrix}$$
Now,
\begin{align*} \sqrt{T}&=|v_1\rangle\langle v_1|+i|v_2\rangle\langle v_2|\\ &=\frac{1}{2}\begin{bmatrix} 1 & 1\\ 1 & 1 \end{bmatrix}+\frac{i}{2}\begin{bmatrix} 1 & -1\\ -1 & 1 \end{bmatrix}\\ &=\begin{bmatrix} \frac{1+i}{2} & \frac{1-i}{2}\\ \frac{1-i}{2} & \frac{1+i}{2} \end{bmatrix} \end{align*}

References:

1. Sheldon Axler, Linear Algebra Done Right, Third Edition, Springer, 2015
2. Michael A. Nielsen and Isaac L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 2000

Let $V$ and $W$ be two vector spaces of dimensions $m$ and $n$, respectively. The tensor product of $V$ and $W$ is a space $V\otimes W$ of dimension $mn$ together with a bilinear map $$\varphi: V\times W\longrightarrow V\otimes W;\ \varphi(v,w)=v\otimes w$$ which satisfy the following universal property: For any vector space $X$ and any bilinear map $\psi: V\times W\longrightarrow X$, there exists uniquely a linear map $\gamma : V\otimes W\longrightarrow X$ such that $\psi(v,w)=\gamma(u\otimes w)$ for all $v\in V$ and $w\in W$.

$$\begin{array}[c]{ccc}V\otimes W & & \\\uparrow\scriptstyle{\varphi} & \scriptstyle{\gamma}\searrow& \\V\times W & \stackrel{\psi}\rightarrow & X\end{array}\ \ \ \gamma\circ\varphi=\psi$$

Often, we use more down to earth definition of the tensor product. Let $\{e_1,\cdots,e_m\}$ and $\{f_1,\cdots,f_n\}$ be bases of $V$ and $W$, respectively. The tensor product $V\otimes W$ is a vector space of dimension $mn$ spanned by the basis $\{e_i\otimes f_j: i=1,\cdots,m,\ j=1,\cdots,n\}$. Let $v\in V$ and $w\in W$. Then $$v=\sum_i v_ie_i\ \mbox{and}\ w=\sum_j w_jf_j$$ The tensor product $v$ and $w$ is then given by $$v\otimes w=\sum_{i,j}v_iw_je_i\otimes f_j$$ It can be easily shown that this definition of the tensor product in terms of prescribed bases satisfies the universality property. Although this definition uses a choice of bases of $V$ and $W$, the tensor product $V\otimes W$ must not depend on a particular choice of bases, i.e. regardless of the choice of bases the resulting tensor product must be the same. This also can be shown easily using some basic properties from linear algebra. I will leave them for exercise for readers.

The tensor product can be used to describe the state of a quantum memory register. A quantum memory register consists of many 2-state systems (Hilbert spaces of qubits). Let $|\psi^{(1)}\rangle$ and $|\psi^{(2)}\rangle$ be qubits associated with two different 2-state systems. In terms of the standard orthogonal basis $\begin{pmatrix}1\\0\end{pmatrix}$ and $\begin{pmatrix}0\\1\end{pmatrix}$ for each 2-state system, we have \begin{align*}|\psi^{(1)}\rangle&=\begin{pmatrix}\omega_0^{(1)}\\\omega_1^{(1)}\end{pmatrix}=\omega_0^{(1)}\begin{pmatrix}1\\0\end{pmatrix}+\omega_1^{(1)}\begin{pmatrix}0\\1\end{pmatrix}\\|\psi^{(2)}\rangle&=\begin{pmatrix}\omega_0^{(2)}\\\omega_1^{(2)}\end{pmatrix}=\omega_0^{(2)}\begin{pmatrix}1\\0\end{pmatrix}+\omega_1^{(2)}\begin{pmatrix}0\\1\end{pmatrix}\end{align*} Define $\otimes$ on the basis members as follows: \begin{align*}|00\rangle&=\begin{pmatrix}1\\0\end{pmatrix}\otimes\begin{pmatrix}1\\0\end{pmatrix}=\begin{pmatrix}1\\0\\0\\0\end{pmatrix},\ |01\rangle=\begin{pmatrix}1\\0\end{pmatrix}\otimes\begin{pmatrix}0\\1\end{pmatrix}=\begin{pmatrix}0\\1\\0\\0\end{pmatrix}\\|10\rangle&=\begin{pmatrix}0\\1\end{pmatrix}\otimes\begin{pmatrix}1\\0\end{pmatrix}=\begin{pmatrix}0\\0\\1\\0\end{pmatrix},\ |11\rangle=\begin{pmatrix}0\\1\end{pmatrix}\otimes\begin{pmatrix}0\\1\end{pmatrix}=\begin{pmatrix}0\\0\\0\\1\end{pmatrix}\end{align*} These four vectors form a basis for a 4-dimensional Hilbert space (a 2-qubit memory register). It follows that \begin{align*}|\psi^{(1)}\rangle\otimes|\psi^{(2)}\rangle&=\omega_0^{(1)}\omega_0^{(2)}|00\rangle+\omega_0^{(1)}\omega_1^{(2)}|01\rangle+\omega_1^{(1)}\omega_0^{(2)}|10\rangle+\omega_1^{(1)}\omega_1^{(2)}|11\rangle\\&=\begin{pmatrix}\omega_0^{(1)}\omega_0^{(2)}\\\omega_0^{(1)}\omega_1^{(2)}\\\omega_1^{(1)}\omega_0^{(2)}\\\omega_1^{(1)}\omega_1^{(2)}\end{pmatrix}\end{align*}Similarly, to describe the state of a 3-qubit memory register, one performs the tensor product $|\psi^{(1)}\rangle\otimes|\psi^{(2)}\rangle\otimes|\psi^{(3)}\rangle$.

Quantum memory registers can store an exponential amount of classical information in only a polynomial number of qubits using the quantum property the Principle of Superposition. For example, consider the two classical memory registers storing complimentary sequences of bits $$\begin{array}{|c|c|c|c|c|c|c|}\hline1 & 0 & 1 & 1 & 0 & 0 &1\\\hline 0 & 1 & 0 & 0 & 1 & 1 & 0\\\hline\end{array}$$ A single quantum memory register can store both sequences simultaneously in an equally weighted superposition of the two states representing each 7-bit input $$\frac{1}{\sqrt{2}}(|1011001\rangle+|0100110\rangle)$$

A matrix can be considered as a vector. For example, a $2\times 2$ matrix $\begin{pmatrix}a & b\\c & d\end{pmatrix}$ can be identified with the vector $(a, b, c, d) \in \mathbb{R}^4$. Hence one can define the tensor product of two matrices, called the Kronecker product, in a similar manner to that of two vectors. For example, $$\begin{pmatrix}a_{11} & a_{12}\\a_{21} & a_{22}\end{pmatrix}\otimes\begin{pmatrix}b_{11} & b_{12}\\b_{21} & b_{22}\end{pmatrix}:=\begin{pmatrix}a_{11}\begin{pmatrix}b_{11} & b_{12}\\b_{21} & b_{22}\end{pmatrix} & a_{12}\begin{pmatrix}b_{11} & b_{12}\\b_{21} & b_{22}\end{pmatrix}\\a_{21}\begin{pmatrix}b_{11} & b_{12}\\b_{21} & b_{22}\end{pmatrix} & a_{22}\begin{pmatrix}b_{11} & b_{12}\\b_{21} & b_{22}\end{pmatrix}\end{pmatrix}\\=\begin{pmatrix}a_{11}b_{11} & a_{11}b_{12} & a_{12}b_{11} & a_{12}b_{12}\\a_{11}b_{21} & a_{11}b_{22} & a_{12}b_{21} & a_{12}b_{22}\\a_{21}b_{11} & a_{21}b_{12} & a_{22}b_{11} & a_{22}b_{12}\\a_{21}b_{21} & a_{21}b_{22} & a_{22}b_{21} & a_{22}b_{22}\end{pmatrix}$$

References:

[1] A. Yu. Kitaev, A. H. Shen and M. N. Vyalyi, Classical and Quantum Computation, Graduate Studies in Mathematics Volume 47, American Mathematical Society, 2002

[2] Colin P. Williams and Scott H. Clearwater, Explorations in Quantum Computing, Springer TELOS, 1998