handouts/Advanced/Introduction to Quantum/parts/01 bits.tex

\section{Probabilistic Bits}


\definition{}

As we already know, a \textit{classical bit} may take the values \texttt{0} and \texttt{1}. \par
We can model this with a two-sided coin, one face of which is labeled \texttt{0}, and the other, \texttt{1}. \par

\vspace{2mm}

Of course, if we toss such a \say{bit-coin,} we'll get either \texttt{0} or \texttt{1}. \par
We'll denote the probability of getting \texttt{0} as $p_0$, and the probability of getting \texttt{1} as $p_1$. \par
As with all probabilities, $p_0 + p_1$ must be equal to 1.


\vfill


\definition{}

Say we toss a \say{bit-coin} and don't observe the result. We now have a \textit{probabilistic bit}, with a probability $p_0$
of being \texttt{0}, and a probability $p_1$ of being \texttt{1}.

\vspace{2mm}

We'll represent this probabilistic bit's \textit{state} as a vector:
$\left[\begin{smallmatrix}
	p_0 \\ p_1
\end{smallmatrix}\right]$ \par
We do \textbf{not} assume this coin is fair, and thus $p_0$ might not equal $p_1$.

\note{
	This may seem a bit redundant: since $p_0 + p_1$, we can always calculate one probability given the other. \\
	We'll still include both probabilities in the state vector, since this provides a clearer analogy to quantum bits.
}


\vfill


\definition{}

The simplest probabilistic bit states are of course $[0]$ and $[1]$, defined as follows:
\begin{itemize}
	\item $[0] = \left[\begin{smallmatrix} 1 \\ 0 \end{smallmatrix}\right]$
	\item $[1] = \left[\begin{smallmatrix} 0 \\ 1 \end{smallmatrix}\right]$
\end{itemize}
That is, $[0]$ represents a bit that we known to be \texttt{0}, \par
and $[1]$ represents a bit we know to be \texttt{1}.

\vfill


\definition{}
$[0]$ and $[1]$ form a \textit{basis} for all possible probabilistic bit states: \par
Every other probabilistic bit can be written as a \textit{linear combination} of $[0]$ and $[1]$:

\begin{equation*}
	\begin{bmatrix} p_0 \\ p_1 \end{bmatrix}
	=
	p_0 \begin{bmatrix} 1 \\ 0 \end{bmatrix} +
	p_1 \begin{bmatrix} 0 \\ 1 \end{bmatrix}
	=
	p_0 [0] + p_1 [1]
\end{equation*}


\vfill
\pagebreak

\problem{}
Every possible state of a probabilistic bit is a two-dimensional vector. \par
Draw all possible states on the axis below.


\begin{center}
	\begin{tikzpicture}[scale = 2.0]
		\fill[color = black] (0, 0) circle[radius=0.05];
		\node[below left] at (0, 0) {$\left[\begin{smallmatrix} 0 \\ 0 \end{smallmatrix}\right]$};

		\draw[->] (0, 0) -- (1.2, 0);
		\node[right] at (1.2, 0) {$p_0$};
		\fill[color = oblue] (1, 0) circle[radius=0.05];
		\node[below] at (1, 0) {$[0]$};

		\draw[->] (0, 0) -- (0, 1.2);
		\node[above] at (0, 1.2) {$p_1$};
		\fill[color = oblue] (0, 1) circle[radius=0.05];
		\node[left] at (0, 1) {$[1]$};
	\end{tikzpicture}
\end{center}

\begin{solution}
	\begin{center}
		\begin{tikzpicture}[scale = 2.0]
			\fill[color = black] (0, 0) circle[radius=0.05];
			\node[below left] at (0, 0) {$\left[\begin{smallmatrix} 0 \\ 0 \end{smallmatrix}\right]$};

			\draw[ored, -, line width = 2] (0, 1) -- (1, 0);


			\draw[->] (0, 0) -- (1.2, 0);
			\node[right] at (1.2, 0) {$p_0$};
			\fill[color = oblue] (1, 0) circle[radius=0.05];
			\node[below] at (1, 0) {$[0]$};

			\draw[->] (0, 0) -- (0, 1.2);
			\node[above] at (0, 1.2) {$p_1$};
			\fill[color = oblue] (0, 1) circle[radius=0.05];
			\node[left] at (0, 1) {$[1]$};
		\end{tikzpicture}
	\end{center}
\end{solution}


\vfill
\pagebreak





\section{Measuring Probabilistic Bits}


\definition{}
As we noted before, a probabilistic bit represents a coin we've tossed but haven't looked at. \par
We do not know whether the bit is \texttt{0} or \texttt{1}, but we do know the probability of both of these outcomes. \par

\vspace{2mm}

If we \textit{measure} (or \textit{observe}) a probabilistic bit, we see either \texttt{0} or \texttt{1}---and thus our
knowledge of its state is updated to either $[0]$ or $[1]$, since we now certainly know what face the coin landed on.

\vspace{2mm}

Since measurement changes what we know about a probabilistic bit, it changes the probabilistic bit's state.
When we measure a bit, it's state \textit{collapses} to either $[0]$ or $[1]$, and the original state of the
bit vanishes. We \textit{cannot} recover the state $[x_0, x_1]$ from a measured probabilistic bit.


\definition{Multiple bits}
Say we have two probabilistic bits, $x$ and $y$, \par
with states
$[x]=[ x_0, x_1]$
and
$[y]=[y_0, y_1]$

\vspace{2mm}

The \textit{compound state} of $[x]$ and $[y]$ is exactly what it sounds like: \par
It is the probabilistic two-bit state $\ket{xy}$, where the probabilities of the first bit are
determined by $[x]$, and the probabilities of the second are determined by $[y]$.




\problem{}<firstcompoundstate>
Say $[x] = [\nicefrac{2}{3}, \nicefrac{1}{3}]$ and $[y] = [\nicefrac{3}{4}, \nicefrac{1}{4}]$. \par
\begin{itemize}[itemsep = 1mm]
	\item If we measure $x$ and $y$ simultaneously, \par
	what is the probability of getting each of \texttt{00}, \texttt{01}, \texttt{10}, and \texttt{11}?


	\item If we measure $y$ first and observe \texttt{1}, \par
	what is the probability of getting each of \texttt{00}, \texttt{01}, \texttt{10}, and \texttt{11}?
\end{itemize}
\note[Note]{$[x]$ and $[y]$ are column vectors, but I've written them horizontally to save space.}


\vfill





\problem{}
With $x$ and $y$ defined as above, find the probability of measuring each of \texttt{00}, \texttt{01}, \texttt{10}, and \texttt{11}.

\vfill

\problem{}
Say $[x] = [\nicefrac{2}{3}, \nicefrac{1}{3}]$ and $[y] = [\nicefrac{3}{4}, \nicefrac{1}{4}]$. \par
What is the probability that $x$ and $y$ produce different outcomes?

\vfill
\pagebreak











\section{Tensor Products}

\definition{Tensor Products}
The \textit{tensor product} of two vectors is defined as follows:
\begin{equation*}
	\begin{bmatrix}
		x_1 \\ x_2
	\end{bmatrix}
	\otimes
	\begin{bmatrix}
		y_1 \\ y_2
	\end{bmatrix}
=
	\begin{bmatrix}
		x_1
		\begin{bmatrix}
			y_1 \\ y_2
		\end{bmatrix}

		\\[4mm]

		x_2
		\begin{bmatrix}
			y_1 \\ y_2
		\end{bmatrix}
	\end{bmatrix}
=
	\begin{bmatrix}
		x_1y_1 \\[1mm]
		x_1y_2 \\[1mm]
		x_2y_1 \\[1mm]
		x_2y_2 \\[0.5mm]
	\end{bmatrix}
\end{equation*}


That is, we take our first vector, multiply the second
vector by each of its components, and stack the result.
You could think of this as a generalization of scalar
mulitiplication, where scalar mulitiplication is a
tensor product with a vector in $\mathbb{R}^1$:
\begin{equation*}
	a
	\begin{bmatrix}
		x_1 \\ x_2
	\end{bmatrix}
=
	\begin{bmatrix}
		a_1
	\end{bmatrix}
	\otimes
	\begin{bmatrix}
		y_1 \\ y_2
	\end{bmatrix}
=
	\begin{bmatrix}
		a_1
		\begin{bmatrix}
			y_1 \\ y_2
		\end{bmatrix}
	\end{bmatrix}
=
	\begin{bmatrix}
		a_1y_1 \\[1mm]
		a_1y_2
	\end{bmatrix}
\end{equation*}

\problem{}
Say $x \in \mathbb{R}^n$ and $y \in \mathbb{R}^m$. \par
What is the dimension of $x \otimes y$?

\vfill

\problem{}<basistp>
What is the pairwise tensor product
$
\Bigl\{
	\left[
	\begin{smallmatrix}
		1 \\ 0 \\ 0
	\end{smallmatrix}
	\right],
	\left[
	\begin{smallmatrix}
		0 \\ 1 \\ 0
	\end{smallmatrix}
	\right],
	\left[
	\begin{smallmatrix}
		0 \\ 0 \\ 1
	\end{smallmatrix}
	\right]
\Bigr\}
\otimes
\Bigl\{
	\left[
	\begin{smallmatrix}
		1 \\ 0
	\end{smallmatrix}
	\right],
	\left[
	\begin{smallmatrix}
		0 \\ 1
	\end{smallmatrix}
	\right]
\Bigr\}
$?

\note{in other words, distribute the tensor product between every pair of vectors.}

\vfill










\problem{}
What is the \textit{span} of the vectors we found in \ref{basistp}? \par
In other words, what is the set of vectors that can be written as linear combinations of the vectors above?

\vfill

Look through the above problems and convince yourself of the following fact: \par
If $a$ is a basis of $A$ and $b$ is a basis of $B$, $a \otimes b$ is a basis of $A \times B$. \par
\note{If you don't understand what this says, ask an instructor. \\ This is the reason we did the last few problems!}

\begin{instructornote}
	\textbf{The idea here is as follows:}

	If $a$ is in $\{\texttt{0}, \texttt{1}\}$ and $b$ is in $\{\texttt{0}, \texttt{1}\}$,
	the values $ab$ can take are
	$\{\texttt{0}, \texttt{1}\} \times \{\texttt{0}, \texttt{1}\} = \{\texttt{00}, \texttt{01}, \texttt{10}, \texttt{11}\}$.

	\vspace{2mm}

	The same is true of any other state set: if $a$ takes values in $A$ and $b$ takes values in $B$, \par
	the compound state $(a,b)$ takes values in $A \times B$.

	\vspace{2mm}

	We would like to do the same with probabilistic bits. \par
	Given bits $\ket{a}$ and $\ket{b}$, how should we represent the state of $\ket{ab}$?
\end{instructornote}

\pagebreak






\problem{}
Say $[x] = [\nicefrac{2}{3}, \nicefrac{1}{3}]$ and $[y] = [\nicefrac{3}{4}, \nicefrac{1}{4}]$. \par
What is $[x] \otimes [y]$? How does this relate to \ref{firstcompoundstate}?

\vfill



\problem{}
The compound state of two vector-form bits is their tensor product. \par
Compute the following. Is the result what we'd expect?
\begin{itemize}
	\item $[0] \otimes [0]$
	\item $[0] \otimes [1]$
	\item $[1] \otimes [0]$
	\item $[1] \otimes [1]$
\end{itemize}
\hint{
	Remember that
	$[0] = \left[\begin{smallmatrix} 1 \\ 0 \end{smallmatrix}\right]$
	and
	$[1] = \left[\begin{smallmatrix} 0 \\ 1 \end{smallmatrix}\right]$.
}


\vfill









\problem{}<fivequant>
Of course, writing $[0] \otimes [1]$ is a bit excessive. We'll shorten this notation to $[01]$. \par

\vspace{2mm}

In fact, we could go further: if we wanted to write the set of bits $[1] \otimes [1] \otimes [0] \otimes [1]$, \par
we could write $[1101]$---but a shorter alternative is $[13]$, since $13$ is \texttt{1101} in binary.

\vspace{2mm}

Write $[5]$ as three-bit probabilistic state. \par

\begin{solution}
	$[5] = [101] = [1] \otimes [0] \otimes [1] = [0,0,0,0,0,1,0,0]^T$ \par
	Notice how we're counting from the top, with $[000] = [1,0,...,0]$ and $[111] = [0, ..., 0, 1]$.
\end{solution}

\vfill






\problem{}
Write the three-bit states $[0]$ through $[7]$ as column vectors. \par
\hint{You do not need to compute every tensor product. Do a few and find the pattern.}






\vfill
\pagebreak

















\section{Operations on Probabilistic Bits}

Now that we can write probabilistic bits as vectors, we can represent operations on these bits
with linear transformations---in other words, as matrices.

\definition{}
Consider the NOT gate, which operates as follows: \par
\begin{itemize}
	\item $\text{NOT}[0] = [1]$
	\item $\text{NOT}[1] = [0]$
\end{itemize}
What should NOT do to a probabilistic bit $[x_0, x_1]$? \par
If we return to our coin analogy, we can think of the NOT operation as
flipping a coin we have already tossed, without looking at it's state.
Thus,
\begin{equation*}
	\text{NOT} \begin{bmatrix}
		x_0 \\ x_1
	\end{bmatrix} = \begin{bmatrix}
		x_1 \\ x_0
	\end{bmatrix}
\end{equation*}


\begin{ORMCbox}{Review: Matrix Multiplication}{black!10!white}{black!65!white}
	Matrix multiplication works as follows:

	\begin{equation*}
		AB =
		\begin{bmatrix}
			1 & 2 \\
			3 & 4 \\
		\end{bmatrix}
		\begin{bmatrix}
			a_0 & b_0 \\
			a_1 & b_1 \\
		\end{bmatrix}
		=
		\begin{bmatrix}
			1a_0 + 2a_1 & 1b_0 + 2b_1 \\
			3a_0 + 4a_1 & 3b_0 + 4b_1 \\
		\end{bmatrix}
	\end{equation*}


	Note that this is very similar to multiplying each column of $B$ by $A$. \par
	The product $AB$ is simply $Ac$ for every column $c$ in $B$:

	\begin{equation*}
		Ac_0 =
		\begin{bmatrix}
			1 & 2 \\
			3 & 4 \\
		\end{bmatrix}
		\begin{bmatrix}
			a_0 \\ a_1
		\end{bmatrix}
		=
		\begin{bmatrix}
			1a_0 + 2a_1 \\
			3a_0 + 4a_1
		\end{bmatrix}
	\end{equation*}

	This is exactly the first column of the matrix product. \par
	Also, note that each element of $Ac_0$ is the dot product of a row in $A$ and a column in $c_0$.
\end{ORMCbox}

\problem{}
Compute the following product:
\begin{equation*}
	\begin{bmatrix}
		1 & 0.5 \\ 0 & 1
	\end{bmatrix}
	\begin{bmatrix}
		3 \\ 2
	\end{bmatrix}
\end{equation*}


\vfill

\generic{Remark:}
Also, recall that every matrix is linear map, and that every linear map may be written as a matrix. \par
We often use the terms \textit{matrix}, \textit{transformation}, and \textit{linear map} interchangably.

\pagebreak


\problem{}
Find the matrix that represents the NOT operation on one probabilistic bit.

\begin{solution}
	\begin{equation*}
		\begin{bmatrix}
			0 & 1 \\ 1 & 0
		\end{bmatrix}
	\end{equation*}
\end{solution}

\vfill


\problem{Extension by linearity}
Say we have an arbitrary operation $A$. \par
If we know how $A$ acts on $[1]$ and $[0]$, can we compute $A[x]$ for an arbitrary state $[x]$? \par
Say $[x] = [x_0, x_1]$.
\begin{itemize}
	\item What is the probability we observe $0$ when we measure $x$?
	\item What is the probability that we observe $M[0]$ when we measure $Mx$?
\end{itemize}

\vfill

\problem{}<linearextension>
Write $M[x_0, x_1]$ in terms of $M[0]$, $M[1]$, $x_0$, and $x_1$.


\begin{solution}
	\begin{equation*}
		M \begin{bmatrix}
			x_0 \\ x_1
		\end{bmatrix}
		=
		x_0 M \begin{bmatrix}
			1 \\ 0
		\end{bmatrix}
		+
		x_1 M \begin{bmatrix}
			0 \\ 1
		\end{bmatrix}
		=
		x_0 M [0] +
		x_1 M [1]
	\end{equation*}
\end{solution}



\vfill

\generic{Remark:}
Every matrix represents a \textit{linear} map, so the following is always true:
\begin{equation*}
	A \times (px + qy) = pAx + qAy
\end{equation*}
\ref{linearextension} is just a special case of this fact.

\pagebreak