📝 Small corrections for final QSW version

docs/handbook/01_intro.md

@@ -8,8 +8,8 @@ Compared to that, most existing software solutions for quantum computing leave t
 
 The _Munich Quantum Toolkit (MQT)_, which is developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/), aims to leverage this latent potential by providing a collection of state-of-the-art design automation methods and software tools for quantum computing.
 Our overarching objective is to provide solutions for design tasks across the entire quantum software stack.
-This entails high-level support for end users in realizing their _applications_ {cite:p}`quetschlichAutomatedFrameworkRealizing2023,quetschlichHybridClassicalQuantum2023,quetschlichPredictingGoodQuantum2023,quetschlichUtilizingResourceEstimation2024,quetschlichApplicationawareQuantumCircuit2024, quetschlichMQTPredictorAutomatic2023`, efficient methods for the _classical simulation_ {cite:p}`zulehnerAdvancedSimulationQuantum2019,zulehnerMatrixVectorVsMatrixMatrix2019,hillmichJustRealThing2020,hillmichAccurateNeededEfficient2020,hillmichApproximatingDecisionDiagrams2022,grurlConsideringDecoherenceErrors2020,grurlStochasticQuantumCircuit2021,grurlNoiseawareQuantumCircuit2023,burgholzerHybridSchrodingerFeynmanSimulation2021,sanderHamiltonianSimulationDecision2023,matoMixeddimensionalQuantumCircuit2023,hillmichConcurrencyDDbasedQuantum2020,burgholzerExploitingArbitraryPaths2022,burgholzerSimulationPathsQuantum2022,burgholzerEfficientConstructionFunctional2021`, _compilation_ {cite:p}`zulehnerEfficientMethodologyMapping2019,hillmichExploitingQuantumTeleportation2021,zulehnerCompilingSUQuantum2019,willeMappingQuantumCircuits2019,burgholzerLimitingSearchSpace2022,pehamDepthoptimalSynthesisClifford2023, schneiderSATEncodingOptimal2023,pehamOptimalSubarchitecturesQuantum2023,quetschlichMQTPredictorAutomatic2023,quetschlichCompilerOptimizationQuantum2023,quetschlichReducingCompilationTime2023,schoenbergerUsingBooleanSatisfiability2024,schoenbergerCyclebasedShuttlingTrappedIon2024,schoenbergerShuttlingScalableTrappedIon2024,schmidHybridCircuitMapping2024,schmidComputationalCapabilitiesCompiler2024,matoAdaptiveCompilationMultilevel2022,matoCompilationEntanglingGates2023,matoCompressionQubitCircuits2023,matoMixeddimensionalQuditState2024,grurlAutomaticImplementationEvaluation2023`, and _verification_ {cite:p}`burgholzerAdvancedEquivalenceChecking2021,burgholzerImprovedDDbasedEquivalence2020, burgholzerPowerSimulationEquivalence2020,burgholzerRandomStimuliGeneration2021, burgholzerVerifyingResultsIBM2020,pehamEquivalenceCheckingParameterized2023,pehamEquivalenceCheckingQuantum2022,pehamEquivalenceCheckingParadigms2022,willeVerificationQuantumCircuits2022` of quantum circuits, tools for _quantum error correction_ {cite:p}`berentDecodingQuantumColor2023,berentSoftwareToolsDecoding2023,strikisQuantumLowdensityParitycheck2023,berentAnalogInformationDecoding2023`, support for _physical design_ {cite:p}`kunasaikaranFrameworkDesignRealization2024`, and more.
-In all these tools, we try to utilize _data structures_ (such as decision diagrams {cite:p}`willeToolsQuantumComputing2022,willeDecisionDiagramsQuantum2023` or the ZX-calculus {cite:p}`vandeweteringZXcalculusWorkingQuantum2020,duncanGraphtheoreticSimplificationQuantum2020`) and _core methods_ (such as reasoning engines {cite:p}`berentSATEncodingQuantum2022`) to facilitate the efficient handling of quantum computations.
+This entails high-level support for end users in realizing their _applications_ {cite:p}`quetschlichAutomatedFrameworkRealizing2023,quetschlichHybridClassicalQuantum2023,quetschlichPredictingGoodQuantum2023,quetschlichUtilizingResourceEstimation2024,quetschlichApplicationawareQuantumCircuit2024, quetschlichMQTPredictorAutomatic2023`, efficient methods for the _classical simulation_ {cite:p}`zulehnerAdvancedSimulationQuantum2019,zulehnerMatrixVectorVsMatrixMatrix2019,hillmichJustRealThing2020,hillmichAccurateNeededEfficient2020,hillmichApproximatingDecisionDiagrams2022,grurlConsideringDecoherenceErrors2020,grurlStochasticQuantumCircuit2021,grurlNoiseawareQuantumCircuit2023,burgholzerHybridSchrodingerFeynmanSimulation2021,sanderHamiltonianSimulationDecision2023,matoMixeddimensionalQuantumCircuit2023,hillmichConcurrencyDDbasedQuantum2020,burgholzerExploitingArbitraryPaths2022,burgholzerSimulationPathsQuantum2022,burgholzerEfficientConstructionFunctional2021`, _compilation_ {cite:p}`zulehnerEfficientMethodologyMapping2019,hillmichExploitingQuantumTeleportation2021,zulehnerCompilingSUQuantum2019,willeMappingQuantumCircuits2019,willeEfficientCorrectCompilation2020,burgholzerLimitingSearchSpace2022,pehamDepthoptimalSynthesisClifford2023, schneiderSATEncodingOptimal2023,pehamOptimalSubarchitecturesQuantum2023,quetschlichCompilerOptimizationQuantum2023,quetschlichReducingCompilationTime2023,schoenbergerUsingBooleanSatisfiability2024,schoenbergerCyclebasedShuttlingTrappedIon2024,schoenbergerShuttlingScalableTrappedIon2024,schmidHybridCircuitMapping2024,schmidComputationalCapabilitiesCompiler2024,matoAdaptiveCompilationMultilevel2022,matoCompilationEntanglingGates2023,matoCompressionQubitCircuits2023,matoMixeddimensionalQuditState2024,grurlAutomaticImplementationEvaluation2023`, and _verification_ {cite:p}`burgholzerAdvancedEquivalenceChecking2021,burgholzerImprovedDDbasedEquivalence2020, burgholzerPowerSimulationEquivalence2020,burgholzerRandomStimuliGeneration2021, burgholzerVerifyingResultsIBM2020,pehamEquivalenceCheckingParameterized2023,pehamEquivalenceCheckingQuantum2022,pehamEquivalenceCheckingParadigms2022,willeVerificationQuantumCircuits2022` of quantum circuits, tools for _quantum error correction_ {cite:p}`berentDecodingQuantumColor2023,berentSoftwareToolsDecoding2023,strikisQuantumLowdensityParitycheck2023,berentAnalogInformationDecoding2023`, support for _physical design_ {cite:p}`kunasaikaranFrameworkDesignRealization2024`, and more.
+In all these tools, we try to utilize _data structures_ (such as decision diagrams {cite:p}`willeToolsQuantumComputing2022,willeDecisionDiagramsQuantum2023,willeVisualizingDecisionDiagrams2021` or the ZX-calculus {cite:p}`vandeweteringZXcalculusWorkingQuantum2020,duncanGraphtheoreticSimplificationQuantum2020`) and _core methods_ (such as reasoning engines {cite:p}`berentSATEncodingQuantum2022`) to facilitate the efficient handling of quantum computations.
 The proposed solutions demonstrate how utilizing design automation expertise can lead to improved efficiency, scalability, and reliability.
 In particular, they illustrate the immense benefits of leveraging expertise in classical circuit and system design rather than starting from scratch.
 All tools developed as part of the MQT are made available as open-source packages on [github.com/cda-tum](https://github.com/cda-tum/).

docs/handbook/02_simulation.md

@@ -17,7 +17,7 @@ Moreover, classical simulation provides means to study quantum error correction
 
 The classical simulation of quantum circuits is commonly conducted by performing consecutive matrix-vector multiplication, which many simulators realize by storing a dense representation of the complete state vector in memory and evolving it correspondingly (see, e.g., {cite:p}`hanerPetabyteSimulation45Qubit2017,doiQuantumComputingSimulator2019,jonesQuESTHighPerformance2018,guerreschiIntelQuantumSimulator2020, wuFullstateQuantumCircuit2019`) or by relying on tensor network methods (see, e.g., {cite:p}`markovSimulatingQuantumComputation2008,villalongaFlexibleHighperformanceSimulator2019,brennanTensorNetworkCircuit2021,vincentJetFastQuantum2022`).
 This approach quickly becomes intractable due to the exponential growth of the quantum state with respect to the number of qubits---quickly rendering such simulations infeasible even on supercomputer clusters.
-Simulation methodologies based on decision diagrams {cite:p}`viamontesImprovingGatelevelSimulation2003,willeDecisionDiagramsQuantum2023,zulehnerAdvancedSimulationQuantum2019` are a promising complementary approach that frequently allows reducing the required memory by exploiting redundancies in the simulated quantum state.
+Simulation methodologies based on decision diagrams {cite:p}`viamontesImprovingGatelevelSimulation2003,willeToolsQuantumComputing2022,willeDecisionDiagramsQuantum2023,willeVisualizingDecisionDiagrams2021` are a promising complementary approach that frequently allows reducing the required memory by exploiting redundancies in the simulated quantum state.
 
 The _MQT_ offers the classical quantum circuit simulator _DDSIM_ that can be used to perform various quantum circuit simulation tasks based on using decision diagrams as a data structure.
 This includes strong and weak simulation {cite:p}`zulehnerAdvancedSimulationQuantum2019,zulehnerMatrixVectorVsMatrixMatrix2019,hillmichJustRealThing2020`, approximation techniques {cite:p}`hillmichAccurateNeededEfficient2020,hillmichApproximatingDecisionDiagrams2022`, noise-aware simulation {cite:p}`grurlConsideringDecoherenceErrors2020,grurlStochasticQuantumCircuit2021,grurlNoiseawareQuantumCircuit2023`, hybrid Schrödinger-Feynman techniques {cite:p}`burgholzerHybridSchrodingerFeynmanSimulation2021`, support for dynamic circuits, the computation of expectation values {cite:p}`sanderHamiltonianSimulationDecision2023`, the simulation of mixed-dimensional systems {cite:p}`matoMixeddimensionalQuantumCircuit2023`, and more {cite:p}`hillmichConcurrencyDDbasedQuantum2020,burgholzerExploitingArbitraryPaths2022,burgholzerSimulationPathsQuantum2022,burgholzerEfficientConstructionFunctional2021`.

docs/handbook/06_implementations.md

@@ -1,6 +1,6 @@
 # Open-Source Implementations
 
-All tools that have been developed as part of the _MQT_ are publicly available on [github.com/cda-tum](https://github.com/cda-tum}{github.com/cda-tum).
+All tools that have been developed as part of the _MQT_ are publicly available on [github.com/cda-tum](https://github.com/cda-tum).
 Many of these tools are powered by MQT Core, which forms the backbone of the entire toolkit.
 It features a comprehensive intermediate representation for quantum computations as well as a state-of-the-art decision diagram package for quantum computing and a dedicated ZX-calculus library.
 

docs/handbook/07_conclusions.md

@@ -11,7 +11,7 @@ As the quantum computing landscape advances towards _Fault-Tolerant Quantum Comp
 
 ```
 
-We thank everyone that contributed to the development of the _Munich Quantum Toolkit_.
+We thank everyone who contributed to the development of the _Munich Quantum Toolkit_.
 Special thanks go to Alwin Zulehner, Stefan Hillmich, Thomas Grurl, Hartwig Bauer, Sarah Schneider, Smaran Adarsh, and Alexander Ploier for their specific contributions in the past.
 
 ```{only} latex

docs/index.md

@@ -20,7 +20,7 @@ Many of the problems to be tackled in that regard are similar to design problems
 The _[Munich Quantum Toolkit (MQT)](https://mqt.readthedocs.io)_ is a collection of software tools for quantum computing developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/) which explicitly utilizes this design automation expertise.
 Our overarching objective is to provide solutions for design tasks across the entire quantum software stack.
 This entails high-level support for end users in realizing their _applications_, efficient methods for the _classical simulation_, _compilation_, and _verification_ of quantum circuits, tools for _quantum error correction_, support for _physical design_, and more.
-These methods are supported by corresponding _data structures_ (such as decision diagrams) and _core methods_ (such as SAT encodings/solvers).
+These methods are supported by corresponding _data structures_ (such as decision diagrams or the ZX-calculus) and _core methods_ (such as SAT encodings/solvers).
 All of the developed tools are available as open-source implementations and are hosted on [github.com/cda-tum](https://github.com/cda-tum).
 
 ````{only} latex

docs/refs.bib

@@ -739,3 +739,19 @@ @article{duncanGraphtheoreticSimplificationQuantum2020
   pages = {279},
   doi = {10.22331/q-2020-06-04-279},
 }
+
+@inproceedings{willeVisualizingDecisionDiagrams2021,
+  title = {Visualizing decision diagrams for quantum computing},
+  booktitle = date,
+  author = {Wille, Robert and Burgholzer, Lukas and Artner, Michael},
+  year = {2021},
+  doi = {10.23919/DATE51398.2021.9474236},
+}
+
+@inproceedings{willeEfficientCorrectCompilation2020,
+  title = {Efficient and correct compilation of quantum circuits},
+  booktitle = iscas,
+  author = {Wille, Robert and Hillmich, Stefan and Burgholzer, Lukas},
+  year = {2020},
+  doi = {10.1109/ISCAS45731.2020.9180791},
+}