72 lines
3.7 KiB
TeX
72 lines
3.7 KiB
TeX
% vim: ft=tex
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\section{Implementation}
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This chapter discusses how the concepts introduced before are implemented
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into a simulator. Futher the infrastructure around the simulation and some
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tools are explained.
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\subsection{Dense State Vector Simulation}
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\subsubsection{Representation of Dense State Vectors}
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Recalling \eqref{eq:ci} any $n$-qbit state can be represented as a
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$2^n$ component vector in the integer state basis. This representation
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has some useful features when it comes to computations:
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\begin{itemize}
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\item{The projection on the integer states is trivial.}
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\item{For any qbit $j$ and $0 \le i \le 2^n-1$ the coefficient $c_i$ is part of the $\ket{1}_j$ amplitude iff
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$i \& (1 << j)$ and part of the $\ket{0}_j$ amplitude otherwise.}
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\item{For a qbit $j$ the coefficients $c_i$ and $c_{i \hat{} (1 << j)}$ are the conjugated coefficients.}
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\end{itemize}
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Where $\hat{}$ is the binary XOR, $\&$ the binary AND and $<<$ the binary leftshift operator.
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While implementing the dense state vectors two key points were allowing a simple and readable
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way to use them and simple access to the states by users that want more information than an
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abstracted view could allow. To meet both requirements the states are implemented as Python objects
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providing abstract features such as normalization checking, checking for sufficient qbit number when applying
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a circuit, computing overlaps with other states, a stringify method and stored measurement results.
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To store the measurement results a NumPy \lstinline{int8} array \cite{numpy_array} is used; this is called
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the classical state.
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The Python states also have a NumPy \lstinline{cdouble} array that stores the quantum mechanical state.
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Using NumPy arrays has the advantage that access to the data is simple and safe while operations
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on the states can be implemented in \lstinline{C} \cite{numpy_ufunc} providing a considerable speedup.
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This quantum mechanical state is the component vector in integer basis therefore it has $2^n$ components.
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Storing those components is acceptable in a range from $1$ to $30$ qbits; above this range the state requires
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space in the order of $1 \mbox{ GiB}$ which is in the range of usual RAM sizes for personal computers. For higher
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qbit numbers moving to high performance computers and other simulators is necessary.
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\subsubsection{Gates}
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Gates on dense state vectors are implemented as NumPy Universal Functions (ufuncs) \cite{numpy_ufunc} mapping a classical
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and a quantum state to a new classical state, a new quantum state and a $64 \mbox{ bit}$ integer indicating what qbits have
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been measured. Using ufuncs has the great advantage that managing memory is done by NumPy and an application programmer
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just has to implement the logic of the function. Because ufuncs are written in \lstinline{C} they provide a considerable
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speedup compared to an implementation in Python.
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The logic of gates is usually easy to implement using the integer basis. The example below implements the Hadamard gate
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\ref{ref:singleqbitgates}:
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\adjustbox{max width=\textwidth}{\lstinputlisting[language=C, firstline=153, lastline=178]{../pyqcs/src/pyqcs/gates/implementations/basic_gates.c}}
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A basic set of gates is implemented in PyQCS:
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\begin{itemize}
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\item{Hadamard $H$ gate.}
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\item{Pauli $X$ or \textit{NOT} gate.}
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\item{Pauli $Z$ gate.}
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\item{The $S$ phase gate.}
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\item{$Z$ rotation $R_\phi$ gate.}
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\item{Controlled $X$ gate: $CX$.}
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\item{Controlled $Z$ gate: $CZ$.}
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\item{The measurement "gate" $M$.}
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\end{itemize}
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To allow the implementation of possible hardware related gates the class \lstinline{GenericGate} takes
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a unitary $2\times2$ matrix as a NumPy \lstinline{cdouble} array and builds a gate from it.
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\subsubsection{Circuits}
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