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Work with vectors and matrices in quantum computing

  • Članek

Some familiarity with vectors and matrices is essential to understand quantum computing. The article Linear algebra for quantum computing provides a brief refresher, and readers who want to dive deeper are recommended to read a standard reference on linear algebra such as Strang, G. (1993). Introduction to linear algebra (Vol. 3). Wellesley, MA: Wellesley-Cambridge Press or an online reference such as Linear Algebra.

Vectors

A column vector (or simply vector) $v$ of dimension (or size) $n$ is a collection of $n$ complex numbers $(v_1,v_2,\ldots,v_n)$ arranged as a column:

$$v =\begin{bmatrix} v_1\\ v_2\\ \vdots\\ v_n \end{bmatrix}$$

The norm of a vector $v$ is defined as $\sqrt{\sum_i |v_i|^2}$. A vector is said to be of unit norm (or alternatively it is called a unit vector) if its norm is $1$. The adjoint of a vector $v$ is denoted $v^\dagger$ and is defined to be the following row vector where $*$ denotes the complex conjugate,

$$\begin{bmatrix}v_1 \\ \vdots \\ v_n \end{bmatrix}^\dagger = \begin{bmatrix}v_1^* & \cdots & v_n^* \end{bmatrix}$$

Notice that there is a distinction between a column vector $v$ and a row vector $v^\dagger$.

Inner product

Two vectors can be multiplied together through the inner product, also known as a dot product or scalar product. As the name implies, the result of the inner product of two vectors is a scalar. The inner product gives the projection of one vector onto another and is invaluable in describing how to express one vector as a sum of other simpler vectors. The inner product between two column vectors $u=(u_1 , u_2 , \ldots , u_n)$ and $v=(v_1 , v_2 , \ldots , v_n)$, denoted $\left\langle u, v\right\rangle$ is defined as

$$ \left\langle u, v\right\rangle = u^\dagger v= \begin{bmatrix}u_1^* & \cdots & u_n^* \end{bmatrix} \begin{bmatrix}v_1\\ \vdots\\ v_n \end{bmatrix} = u_1^{*} v_1 + \cdots + _n^{*} v_n. $$

This notation also allows the norm of a vector $v$ to be written as $\sqrt{\langle v, v\rangle}$.

A vector can be multiplied with a number $c$ to form a new vector whose entries are multiplied by $c$. You can also add two vectors $u$ and $v$ to form a new vector whose entries are the sum of the entries of $u$ and $v$. These operations are the following:

$$\mathrm{If}~u =\begin{bmatrix} u_1\\ u_2\\ \vdots\\ u_n \end{bmatrix}~\mathrm{and}~ v =\begin{bmatrix} v_1\\ v_2\\ \vdots\\ v_n \end{bmatrix},~\mathrm{then}~ au+bv =\begin{bmatrix} au_1+bv_1\\ au_2+bv_2\\ \vdots\\ au_n+bv_n \end{bmatrix}. $$

A matrix of size $m \times n$ is a collection of $mn$ complex numbers arranged in $m$ rows and $n$ columns as shown below:

$M = \begin{bmatrix} M_{11} ~~ M_{12} ~~ \cdots ~~ M_{1n}\\ M_{21} ~~ M_{22} ~~ \cdots ~~ M_{2n}\\ \ddots\\ M_{m1} ~~ M_{m2} ~~ \cdots ~~ M_{mn}\\ \end{bmatrix}.$

Note that a vector of dimension $n$ is simply a matrix of size $n \times 1$. As with vectors, a matrix can be multiplied with a number $c$ to obtain a new matrix where every entry is multiplied with $c$, and two matrices of the same size can be added to produce a new matrix whose entries are the sum of the respective entries of the two matrices.

Matrix multiplication

You can also multiply two matrices $M$ of dimension $m\times n$ and $N$ of dimension $n \times p$ to get a new matrix $P$ of dimension $m \times p$ as follows:

$$ \begin{align} &\begin{bmatrix} M_{11} ~~ M_{12} ~~ \cdots ~~ M_{1n}\\ M_{21} ~~ M_{22} ~~ \cdots ~~ M_{2n}\\ \ddots\\ M_{m1} ~~ M_{m2} ~~ \cdots ~~ M_{mn} \end{bmatrix} \begin{bmatrix} N_{11} ~~ N_{12} ~~ \cdots ~~ N_{1p}\\ N_{21} ~~ N_{22} ~~ \cdots ~~ N_{2p}\\ \ddots\\ N_{n1} ~~ N_{n2} ~~ \cdots ~~ N_{np} \end{bmatrix}=\begin{bmatrix} P_{11} ~~ P_{12} ~~ \cdots ~~ P_{1p}\\ P_{21} ~~ P_{22} ~~ \cdots ~~ P_{2p}\\ \ddots\\ P_{m1} ~~ P_{m2} ~~ \cdots ~~ P_{mp} \end{bmatrix} \end{align} $$

where the entries of $P$ are $P_{ik} = \sum_j M_{ij}N_{jk}$. For example, the entry $P_{11}$ is the inner product of the first row of $M$ with the first column of $N$. Note that since a vector is simply a special case of a matrix, this definition extends to matrix-vector multiplication.

All the matrices we consider will either be square matrices, where the number of rows and columns are equal, or vectors, which corresponds to only $1$ column. One special square matrix is the identity matrix, denoted $\mathbb{I}$, which has all its diagonal elements equal to $1$ and the remaining elements equal to $0$:

$\mathbb{I}=\begin{bmatrix} 1 ~~ 0 ~~ \cdots ~~ 0\\ 0 ~~ 1 ~~ \cdots ~~ 0\\ ~~ \ddots\\ 0 ~~ 0 ~~ \cdots ~~ 1 \end{bmatrix}.$

For a square matrix $A$, a matrix $B$ is its inverse if $AB = BA = \mathbb{I}$. The inverse of a matrix need not exist, but when it exists it is unique and we denote it $A^{-1}$.

For any matrix $M$, the adjoint or conjugate transpose of $M$ is a matrix $N$ such that $N_{ij} = M_{ji}^*$. The adjoint of $M$ is usually denoted $M^\dagger$. A matrix $U$ is unitary if $UU^\dagger = U^\dagger U = \mathbb{I}$ or equivalently, $U^{-1} = U^\dagger$. One important property of unitary matrices is that they preserve the norm of a vector. This happens because

$\langle v,v \rangle=v^\dagger v = v^\dagger U^{-1} U v = v^\dagger U^\dagger U v = \langle U v, U v\rangle.$

A matrix $M$ is said to be Hermitian if $M=M^\dagger$.

Tensor product

Another important operation is the Kronecker product, also called the matrix direct product or tensor product. Note that the Kronecker product is distinguished from matrix multiplication, which is an entirely different operation. In quantum computing theory, tensor product is commonly used to denote the Kronecker product.

Consider the two vectors $v=\begin{bmatrix}a \\ b \end{bmatrix} $ and $u =\begin{bmatrix} c \\ d \\ e \end{bmatrix} $. Their tensor product is denoted as $v \otimes u$ and results in a block matrix.

$$ \begin{bmatrix} a \\ b \end{bmatrix} \otimes \begin{bmatrix} c \\ d \\ e \end{bmatrix} = \begin{bmatrix} a \begin{bmatrix} c \\ d \\ e \end{bmatrix} \\[1.5em] b \begin{bmatrix} c \\ d \\ e\end{bmatrix} \end{bmatrix} = \begin{bmatrix} a c \\ a d \\ a e \\ b c \\ b d \\ be\end{bmatrix} $$

Notice that tensor product is an operation on two matrices or vectors of arbitrary size. The tensor product of two matrices $M$ of size $m\times n$ and $N$ of size $p \times q$ is a larger matrix $P=M\otimes N$ of size $mp \times nq$, and is obtained from $M$ and $N$ as follows:

$$ \begin{align} M \otimes N &= \begin{bmatrix} M_{11} ~~ \cdots ~~ M_{1n} \\ \ddots\\ M_{m1} ~~ \cdots ~~ M_{mn} \end{bmatrix} \otimes \begin{bmatrix} N_{11} ~~ \cdots ~~ N_{1q}\\ \ddots\\ N_{p1} ~~ \cdots ~~ N_{pq} \end{bmatrix}\\ &= \begin{bmatrix} M_{11} \begin{bmatrix} N_{11} ~~ \cdots ~~ N_{1q}\\ \ddots\\ N_{p1} ~~ \cdots ~~ N_{pq} \end{bmatrix}~~ \cdots ~~ M_{1n} \begin{bmatrix} N_{11} ~~ \cdots ~~ N_{1q}\\ \ddots\\ N_{p1} ~~ \cdots ~~ N_{pq} \end{bmatrix}\\ \ddots\\ M_{m1} \begin{bmatrix} N_{11} ~~ \cdots ~~ N_{1q}\\ \ddots\\ N_{p1} ~~ \cdots ~~ N_{pq} \end{bmatrix}~~ \cdots ~~ M_{mn} \begin{bmatrix} N_{11} ~~ \cdots ~~ N_{1q}\\ \ddots\\ N_{p1} ~~ \cdots ~~ N_{pq} \end{bmatrix} \end{bmatrix}. \end{align} $$

This is better demonstrated with an example: $$ \begin{bmatrix} a\ b \\ c\ d \end{bmatrix} \otimes \begin{bmatrix} e\ f\\ g\ h \end{bmatrix} = \begin{bmatrix} a\begin{bmatrix} e\ f\\ g\ h \end{bmatrix} b\begin{bmatrix} e\ f\\ g\ h \end{bmatrix} \\[1em] c\begin{bmatrix} e\ f\\ g\ h \end{bmatrix} d\begin{bmatrix} e\ f\\ g\ h \end{bmatrix} \end{bmatrix} = \begin{bmatrix} ae\ af\ be\ bf \\ ag\ ah\ bg\ bh \\ ce\ cf\ de\ df \\ cg\ ch\ dg\ dh \end{bmatrix}. $$

A final useful notational convention surrounding tensor products is that, for any vector $v$ or matrix $M$, $v^{\otimes n}$ or $M^{\otimes n}$ is short hand for an $n$-fold repeated tensor product. For example:

\begin{align} &\begin{bmatrix} 1 \\ 0 \end{bmatrix}^{\otimes 1} = \begin{bmatrix} 1 \\ 0 \end{bmatrix}, \qquad\begin{bmatrix} 1 \\ 0 \end{bmatrix}^{\otimes 2} = \begin{bmatrix} 1 \\ 0 \\0 \\0 \end{bmatrix}, \qquad\begin{bmatrix} 1 \\ -1 \end{bmatrix}^{\otimes 2} = \begin{bmatrix} 1 \\ -1 \\-1 \\1 \end{bmatrix}, \\ &\begin{bmatrix} 0 & 1 \\ 1& 0 \end{bmatrix}^{\otimes 1}= \begin{bmatrix} 0& 1 \\ 1& 0 \end{bmatrix}, \qquad\begin{bmatrix} 0 & 1 \\ 1& 0 \end{bmatrix}^{\otimes 2}= \begin{bmatrix} 0 &0&0&1 \\ 0 &0&1&0 \\ 0 &1&0&0\\ 1 &0&0&0\end{bmatrix}. \end{align}

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