fixed some stuff as suggested by Simon

performance/plot_scaling_qbits_linear.py

@@ -12,18 +12,18 @@ with open("qbit_scaling_meta.json") as fin:
 
 
 h0 = plt.errorbar(results_naive[:, 0], results_naive[:, 2], results_naive[:, 3]
-                , label=f"Dense Vector Simulator $N_c={int(results_naive[:, 1][0])}$ circuits"
+                , label=f"Dense Vector Simulator $N_c={int(results_naive[:, 1][0])}$ Circuits"
                 , marker="o"
                 , color="black")
 h1 = plt.errorbar(results_graph[:, 0], results_graph[:, 2], results_graph[:, 3]
-                , label=f"Graphical Simulator $N_c={int(results_graph[:, 1][0])}$ circuits"
+                , label=f"Graphical Simulator $N_c={int(results_graph[:, 1][0])}$ Circuits"
                 , marker="^"
                 , color="black")
 
 plt.legend(handles=[h0, h1])
 plt.xlabel("Number of Qbits $N_q$")
-plt.ylabel("Execution time per circuit [s]")
+plt.ylabel("Execution Time per Circuit [s]")
-plt.title(f"Execution Time for ${meta['ngates_per_qbit']}\\times N_q$ Gates with random Circuits (rescaled)")
+plt.title(f"Execution Time for ${meta['ngates_per_qbit']}\\times N_q$ Gates with Random Circuits (Rescaled)")
 
 plt.savefig("scaling_qbits_linear.png", dpi=400)
 plt.show()

performance/plot_scaling_qbits_log.py

@@ -12,19 +12,19 @@ with open("qbit_scaling_meta.json") as fin:
 
 
 h0 = plt.errorbar(results_naive[:, 0], results_naive[:, 2], results_naive[:, 3]
-                , label=f"Dense Vector Simulator $N_c={int(results_naive[:, 1][0])}$ circuits"
+                , label=f"Dense Vector Simulator $N_c={int(results_naive[:, 1][0])}$ Circuits"
                 , marker="o"
                 , color="black")
 h1 = plt.errorbar(results_graph[:, 0], results_graph[:, 2], results_graph[:, 3]
-                , label=f"Graphical Simulator $N_c={int(results_graph[:, 1][0])}$ circuits"
+                , label=f"Graphical Simulator $N_c={int(results_graph[:, 1][0])}$ Circuits"
                 , marker="^"
                 , color="black")
 
 plt.yscale("log")
 plt.legend(handles=[h0, h1])
 plt.xlabel("Number of Qbits $N_q$")
-plt.ylabel("Execution time per circuit [s]")
+plt.ylabel("Execution Time per Circuit [s]")
-plt.title(f"Execution Time for ${meta['ngates_per_qbit']}\\times N_q$ Gates with random Circuits (rescaled)")
+plt.title(f"Execution Time for ${meta['ngates_per_qbit']}\\times N_q$ Gates with Random Circuits (Rescaled)")
 
 plt.savefig("scaling_qbits_log.png", dpi=400)
 plt.show()

performance/regimes/plot_scaling_circuits_50qbit_linear.py

@@ -16,8 +16,8 @@ h0 = plt.errorbar(results_graph0[:, 0], results_graph0[:, 2], results_graph0[:,
                 , color="black")
 
 plt.legend(handles=[h0])
-plt.xlabel("Number of gates in circuit")
+plt.xlabel("Number of Gates in Circuit")
-plt.ylabel("Execution time per circuit [s]")
+plt.ylabel("Execution Time per Circuit [s]")
 plt.title(f"Execution Time for Random Circuits")
 
 plt.savefig("scaling_circuits_50qbit_linear.png", dpi=400)

performance/regimes/plot_scaling_circuits_linear.py

@@ -21,8 +21,8 @@ h1 = plt.errorbar(results_graph1[:, 0], results_graph1[:, 2], results_graph1[:,
                 , color="black")
 
 plt.legend(handles=[h0, h1])
-plt.xlabel("Number of gates in circuit")
+plt.xlabel("Number of Gates in Circuit")
-plt.ylabel("Execution time per circuit [s]")
+plt.ylabel("Execution Time per Circuit [s]")
 plt.title(f"Execution Time for Random Circuits")
 
 plt.savefig("scaling_circuits_linear.png", dpi=400)

performance/regimes/plot_scaling_circuits_measurements_linear.py

@@ -21,8 +21,8 @@ h1 = plt.errorbar(results_graph1[:, 0], results_graph1[:, 2], results_graph1[:,
                 , color="black")
 
 plt.legend(handles=[h0, h1])
-plt.xlabel("Number of gates in circuit")
+plt.xlabel("Number of Gates in Circuit")
-plt.ylabel("Execution time per circuit [s]")
+plt.ylabel("Execution Time per Circuit [s]")
 plt.title(f"Execution Time for Random Circuits with Random Measurements")
 
 plt.savefig("scaling_circuits_measurements_linear.png", dpi=400)

thesis/chapters/quantum_computing.tex

@@ -96,7 +96,7 @@ integer states
 
 \begin{equation}
     \label{eq:ci}
-    \ket{\psi} = \sum\limits_{i = 0}^{2^n - 1} c_i \ket{i} .
+    \ket{\psi} = \sum\limits_{i = 0}^{2^n - 1} c_i \ket{i}
 \end{equation}
 
 with the normation condition
@@ -176,13 +176,13 @@ The matrix representation of $CX$ and $CZ$ for two qbits is given by
 
 \begin{postulate}
     Let 
-    $$\ket{\psi} = \alpha\ket{\phi_1} \otimes \ket{1}_j + \beta\ket{\phi_0} \otimes \ket{0}_j$$ 
+    \begin{equation}\ket{\psi} = \alpha\ket{\phi_1} \otimes \ket{1}_j + \beta\ket{\phi_0} \otimes \ket{0}_j\end{equation} 
     be a  $n$-qbit state
     where $\ket{1}_j, \ket{0}_j$ denote the $j$-th qbit state and $|\alpha|^2 + |\beta|^2 = 1$. 
     Then the measurement of the $j$-th qbit will yield 
-    $$\ket{\phi_1} \otimes \ket{1}_j$$ 
+    \begin{equation}\ket{\phi_1} \otimes \ket{1}_j\end{equation} 
     with probability $|\alpha|^2$ and 
-    $$\ket{\phi_0} \otimes \ket{0}_j$$
+    \begin{equation}\ket{\phi_0} \otimes \ket{0}_j\end{equation}
     with probability $|\beta|^2$ \cite{nielsen_chuang_2010}.  This is called collapse of the wave function.
 \end{postulate}
 

thesis/main.tex

@@ -39,7 +39,8 @@
 \title{An Efficient Quantum Computing Simulator using a Graphical Description for Many-Qbit Systems \\
 \large Bachelor Thesis}
 \author[1]{Daniel Knüttel}
-\affil[1]{Institute I - Theoretical Physics, University of Regensburg}
+\affil[1]{Institute I - Theoretical Physics, University of Regensburg\\
+Supervised by Prof. Dr. Christoph Lehner}
 \date{10.04.2020} 
 \begin{document} 
 \maketitle