Source file cleanup

content/background.typ

@@ -1,70 +0,0 @@
-#import "@preview/fletcher:0.5.1": diagram, node, edge
-#import "@preview/gentle-clues:0.9.0": example
-#import "@preview/quill:0.3.0": quantum-circuit, lstick, rstick, ctrl, targ, mqgate, meter
-
-
-= Background
-== Quantum Computation and Quantum Circuits
-A quantum computer is a device that performs calculations by using certain phenomena of quantum mechanics.
-The algorithms that run on this device are specified in quantum circuits. 
-
-#example[
-  @example_qc shows a simple quantum circuit that implements a specific quantum algorithm.
-
-  #figure(
-    quantum-circuit(
-      lstick($|0〉$), $H$, mqgate($U$, n: 2, width: 5em, inputs: ((qubit: 0, label: $x$), (qubit: 1, label: $y$)), outputs: ((qubit: 0, label: $x$), (qubit: 1, label: $y plus.circle f(x)$))), $H$, meter(), [\ ],
-      lstick($|1〉$), $H$, 1, 1, 1
-    ),
-    caption: [A quantum circuit implementing the Deutsch-Jozsa algorithm]
-  ) <example_qc>
-]
-
-
-== Decision Diagrams
-Decision diagrams in general are directed acyclical graphs, that may be used to express control flow through a series of conditions.
-It consists of a set of decision nodes and terminal nodes.
-The decision nodes represent an arbitrary decision based on an input value and may thus have any number of outgoing edges.
-The terminal nodes represent output values and may not have outgoing edges.
-
-A @bdd is a specific kind of decision diagram, where there are two terminal nodes (0 and 1) and each decision node has two outgoing edges, depending solely on a single bit of an input value.
-@bdd[s] may be used to represent any boolean function.
-
-#example[
-  Example @bdd[s] implementing boolean functions with an arity of $2$ are show in @example_bdd_xor and @example_bdd_and.
-
-  #figure(
-    diagram(
-      node-stroke: .1em,
-      node((0, 0), [$x_0$], radius: 1em),
-      edge((0, 0), (-1, 1), [0], "->"),
-      edge((0, 0), (1, 1), [1], "->"),
-      node((-1, 1), [$x_1$], radius: 1em),
-      node((1, 1), [$x_1$], radius: 1em),
-      edge((-1, 1), (-1, 2), [0], "->"),
-      edge((-1, 1), (1, 2), [1], "->"),
-      edge((1, 1), (1, 2), [0], "->"),
-      edge((1, 1), (-1, 2), [1], "->"),
-      node((-1, 2), [$0$]),
-      node((1, 2), [$1$]),
-    ),
-    caption: [A @bdd for an XOR gate.]
-  ) <example_bdd_xor>
-
-  #figure(
-    diagram(
-      node-stroke: .1em,
-      node((1, 0), [$x_0$], radius: 1em),
-      edge((1, 0), (0, 2), [0], "->"),
-      edge((1, 0), (1, 1), [1], "->"),
-      node((1, 1), [$x_1$], radius: 1em),
-      edge((1, 1), (0, 2), [0], "->"),
-      edge((1, 1), (1, 2), [1], "->"),
-      node((0, 2), [$0$]),
-      node((1, 2), [$1$]),
-    ),
-    caption: [A @bdd for an AND gate.]
-  ) <example_bdd_and>
-]
-
-

content/benchmarks.typ

@@ -1,16 +0,0 @@
-#import "@preview/tablex:0.0.8": tablex
-#import "@preview/unify:0.6.0": qty
-
-= Benchmarks
-== Google Benchmark
-
-== MQT QCEC Bench
-To generate test cases for the application schemes, @mqt Bench was used. @quetschlich2023mqtbench
-
-#tablex(
-  columns: (1fr, 1fr, 1fr),
-  rows: (auto, auto, auto),
-  [*Benchmark Name*], [*Diff Run Time*], [*Proportional Run Time*],
-  [DJ], [$qty("1.2e-6", "s")$], [$qty("1.5e-6", "s")$],
-  [Grover], [$qty("1.3e-3", "s")$], [$qty("1.7e-3", "s")$]
-)

content/conclusion.typ

@@ -1,35 +0,0 @@
-#import "@preview/glossarium:0.4.1": * 
-
-= Comparison of @smhdt and @bach
-
-During the course of this work, we took a look at two distinct helper-data algorithms: the S-Metric Helper Data method and the newly presented method of optimization through Boundary Adaptive Clustering with helper-data. 
-
-The S-Metric method will always outperform BACH considering the amount of helper data needed for operation. 
-This comes from the nature of S-Metric quickly approaching an optimal @ber for a certain bit width and not improving any further for higher amounts of metrics. 
-
-Comparing both formulas for the extracted-bits to helper-data-bits ratio for both methods we can quickly see that S-Metric will always yield more extracted bits per helper-data bit than BACH. 
-
-Considering #glspl("ber"), S-Metric does outperform BACH for smaller symbol widths.
-But while the error rate for higher order quantization rises exponentially for higher-order bit quantizations, the #glspl("ber") of BACH do seem to rise rather linear than exponentially for higher-order bit quantizations. 
-This behaviour might be attributed to the general procedure of shaping the input values for the quantizer in such a way that they are clustered around the center of a quantizer step, which is a property that carries on for higher order bit quantizations.
-
-
-We can now compare both the S-Metric Helper Data method and the newly presented method of optimization through Boundary Adaptive Clustering with Helper data based on the @ber and the amount of helper data bits, more specifically the extracted bit to helper data bit ratio $cal(r)$.
-The ratios $cal(r)$ for both methods are defined as: 
-$
-cal(r)_"SMHD" = frac(M, log_2(S))\
-cal(r)_"BACH" = frac(M, N-1)
-$
-
-A good outline to compare both performances of @bach and @smhdt is if $cal(r) = 1$ for varying values of $M$. 
-
-#figure(
-  table(
-    columns: 7,
-
-    [*M*], [$1$], [$2$], [$3$], [$4$], [$5$], [$6$],
-    [*@ber @smhdt*], [$8 dot 10^(-6)$], [$4 dot 10^(-5)$], [$1 dot 10^(-3)$], [$0.02$], [$0.08$], [$0.15$],
-    [*@ber @bach*], [$0.09$], [$0.05$], [$0.05$], [$0.22$], [$0.23$], [$0.23$]
-  ),
-  caption: [#glspl("ber") for @bach and @smhdt configurations that equal the bit width of the quantized symbol and the amount of helper data bits]
-)

content/implementation.typ

@@ -1,36 +0,0 @@
-#import "@preview/lovelace:0.3.0": pseudocode-list
-
-= Implementation
-== Visualisation
-Initially, a visualisation of the diff algorithms applied to quantum circuits was created to assess their usefulness in equivalence checking.
-Additionally, this served as exercise to better understand the algorithms to be used for the implementation in @qcec.
-
-
-== QCEC Application Scheme
-The Myers' Algorithm was implemented as an application scheme in @qcec.
-
-#figure(
-  block(
-    pseudocode-list[
-      + do something
-      + do something else
-      + *while* still something to do
-        + do even more
-        + *if* not done yet *then*
-          + wait a bit
-          + resume working
-        + *else*
-          + go home
-        + *end*
-      + *end*
-    ],
-    width: 100%
-  ),
-  caption: [Myers' algorithm.]
-) <myers_algorithm>
-
-
-
-== QCEC Benchmarking Tool
-As @qcec doesn’t have built-in benchmarks, a benchmarking tool was developed to test different configurations on various circuit pairs.
-

content/state.typ

@@ -1,3 +0,0 @@
-= State of the Art
-There are a variety of existing approaches to providing a suitable oracle for quantum circuit equivalence checking based on @dd[s].
-@qcec currently implements gate-cost, lookahead, one-to-one, proportional and sequential application schemes. @burgholzer2021ec