Publication list

Supervised Master's theses of Korbinian Staudacher

1. Alexandru Duca. Phase Gadget Reduction in Graph-Like ZX-Diagrams via Spider Nest Identities. 9 2025.
   BibTeX Entry

   @misc{duca25, author = {Alexandru Duca}, title = {{Phase} {Gadget} {Reduction} in {Graph-Like} {ZX-Diagrams} via {Spider} {Nest} {Identities}}, year = {2025}, key = {duca25}, month = {9}, school = {Ludwig-Maximilians-Universität München}, supervisors = {Korbinian Staudacher}, type = {Masterthesis}, }
2. Jakob Ritter. Simulating Measurement Based Quantum Computing using Tensor Networks. 10 2024.
   BibTeX Entry

   @misc{ritt24, author = {Jakob Ritter}, title = {{Simulating} {Measurement} {Based} {Quantum} {Computing} using {Tensor} {Networks}}, year = {2024}, key = {ritt24}, month = {10}, school = {Ludwig-Maximilians-Universität München}, supervisors = {Korbinian Staudacher and Florian Krötz}, type = {Masterthesis}, }
3. Daniel Alexander Leidreiter. Investigating Evolving Ansatz VQE Algorithms for Job Shop Scheduling. 7 2024.
   BibTeX Entry

   @misc{leid24, author = {Daniel Alexander Leidreiter}, title = {{Investigating} {Evolving} {Ansatz} {VQE} {Algorithms} for {Job} {Shop} {Scheduling}}, year = {2024}, key = {leid24}, month = {7}, school = {Ludwig-Maximilians-Universität München}, supervisors = {Korbinian Staudacher and Xiao-Ting Michelle To}, type = {Masterthesis}, }
4. Wanja Sajko. Exploring the Potential of Filtering Variational Quantum Eigensolvers for Job Scheduling. 11 2023. PDF
   Abstract

   Job scheduling is a complex optimization problem with multiple variables, constraints, and goals. Solving such problems using classical computing can be challenging, since they are NP-complete and real-world instances can be quite large. Quantum computing is a promising solution, as it is theoretically faster than classical computing for certain types of problems. In this thesis, we use the filtering variational quantum eigensolver (F-VQE), a parameter- ized quantum algorithm, to solve a simplified real-world scheduling problem. The F-VQE algorithm optimizes solutions by filtering out unpromising ones and using a classical op- timization routine to refine the remaining solutions. Although the F-VQE algorithm is based on a paper by Amaro et al., it has not yet been fully evaluated for solving scheduling problems. While VQEs have been successful in solving combinatorial optimiza- tion problems, we seek to assess the performance of F-VQE in solving scheduling problems. We have two objectives in this research: firstly, to enhance and analyze the F-VQE al- gorithm, and secondly, to evaluate the potential of quantum computing in solving complex scheduling problems. To accomplish this, we will compare the performance of the F-VQE algorithm with other quantum and classical approaches for solving real-world scheduling problems. This will provide valuable insights into the effectiveness of quantum comput- ing for solving these problems, as well as identify potential improvements to the F-VQE algorithm. We delve deeper into the F-VQE algorithm to identify potential areas for improvement. We will examine various ansatz designs, different filtering strategies, and encoding techniques. Worthwhile additions are implemented and tested against. To compare the F-VQE algorithm’s performance with other variational quantum algo- rithms and an approach using Grover’s algorithm, which is already implemented by the DLR. We evaluate the efficiency, scalability, and quality of solutions provided by each algo- rithm and discuss the potential benefits and drawbacks of the F-VQE for solving real-world scheduling problems.
   BibTeX Entry

   @misc{sajk23, author = {Wanja Sajko}, title = {{Exploring} the {Potential} of {Filtering} {Variational} {Quantum} {Eigensolvers} for {Job} {Scheduling}}, year = {2023}, pdf = {https://bib.nm.ifi.lmu.de/pdf/sajk23.pdf}, abstract = {Job scheduling is a complex optimization problem with multiple variables, constraints, and goals. Solving such problems using classical computing can be challenging, since they are NP-complete and real-world instances can be quite large. Quantum computing is a promising solution, as it is theoretically faster than classical computing for certain types of problems. In this thesis, we use the filtering variational quantum eigensolver (F-VQE), a parameter- ized quantum algorithm, to solve a simplified real-world scheduling problem. The F-VQE algorithm optimizes solutions by filtering out unpromising ones and using a classical op- timization routine to refine the remaining solutions. Although the F-VQE algorithm is based on a paper by Amaro et al., it has not yet been fully evaluated for solving scheduling problems. While VQEs have been successful in solving combinatorial optimiza- tion problems, we seek to assess the performance of F-VQE in solving scheduling problems. We have two objectives in this research: firstly, to enhance and analyze the F-VQE al- gorithm, and secondly, to evaluate the potential of quantum computing in solving complex scheduling problems. To accomplish this, we will compare the performance of the F-VQE algorithm with other quantum and classical approaches for solving real-world scheduling problems. This will provide valuable insights into the effectiveness of quantum comput- ing for solving these problems, as well as identify potential improvements to the F-VQE algorithm. We delve deeper into the F-VQE algorithm to identify potential areas for improvement. We will examine various ansatz designs, different filtering strategies, and encoding techniques. Worthwhile additions are implemented and tested against. To compare the F-VQE algorithm’s performance with other variational quantum algo- rithms and an approach using Grover’s algorithm, which is already implemented by the DLR. We evaluate the efficiency, scalability, and quality of solutions provided by each algo- rithm and discuss the potential benefits and drawbacks of the F-VQE for solving real-world scheduling problems.}, key = {sajk23}, month = {11}, school = {Ludwig-Maximilians-Universität München}, supervisors = {Korbinian Staudacher and Xiao-Ting Michelle To}, type = {Masterthesis}, }
5. Marcel Quanz. Efficient Parallel Quantum Circuit Optimization using the ZX Calculus. 10 2023.
   Abstract

   Through the quantum effects of the so-called 'qubit', quantum computers are potentially able to solve a larger problem class than classical computers in polynomial time, called bounded-error quantum polynomial time (BQP). However, the practicality of scaling quantum computers to tackle larger problems comes with formidable challenges – increased costs, reduced accuracy, as well as longer design and construction times. These challenges can render a quantum computing design infeasible, emphasizing the critical need to ensure quantum circuits are only as large as they are required to be. In order to reduce the size of the circuit, various optimization techniques can be employed. Current quantum circuit optimization algorithms transform circuits into a more general representation in the ZX-calculus, perform various simplification operations on it, and then extract a reduced, but equivalent quantum circuit. Out of these, the simplification and extraction require the greatest amount of time, potentially even outclassing the computational class of BQP. Our research explores methods to parallelize the quantum circuit extraction process, rendering it suitable for high-performance systems. We present our implementation of a parallelized quantum circuit extraction algorithm, alongside surprising findings that have emerged during our work and evaluation, offering avenues for further investigation and refinement.
   BibTeX Entry

   @misc{quan23, author = {Marcel Quanz}, title = {{Efficient} {Parallel} {Quantum} {Circuit} {Optimization} using the {ZX} {Calculus}}, year = {2023}, abstract = {Through the quantum effects of the so-called `qubit', quantum computers are potentially able to solve a larger problem class than classical computers in polynomial time, called bounded-error quantum polynomial time (BQP). However, the practicality of scaling quantum computers to tackle larger problems comes with formidable challenges – increased costs, reduced accuracy, as well as longer design and construction times. These challenges can render a quantum computing design infeasible, emphasizing the critical need to ensure quantum circuits are only as large as they are required to be. In order to reduce the size of the circuit, various optimization techniques can be employed. Current quantum circuit optimization algorithms transform circuits into a more general representation in the ZX-calculus, perform various simplification operations on it, and then extract a reduced, but equivalent quantum circuit. Out of these, the simplification and extraction require the greatest amount of time, potentially even outclassing the computational class of BQP. Our research explores methods to parallelize the quantum circuit extraction process, rendering it suitable for high-performance systems. We present our implementation of a parallelized quantum circuit extraction algorithm, alongside surprising findings that have emerged during our work and evaluation, offering avenues for further investigation and refinement.}, key = {quan23}, month = {10}, school = {Ludwig-Maximilians-Universität München}, supervisors = {Karl Fuerlinger and Korbinian Staudacher}, type = {Masterthesis}, }
6. Jakob Murauer. Analyzing word predictions by quantum natural language processing. 7 2023. PDF
   Abstract

   Quantum natural language processing is an emerging field, that combines principles of quantum computing with natural language processing with the aim to enhance the capa- bilities to process and analyze human language. In classical natural language processing, word prediction is an ingredient of the pretraining task for large language models. This forms the motivation behind the exploration of a word prediction task in QNLP. This thesis deals with the implementation of a word prediction task in quantum nat- ural language processing using a mathematical framework, particularly the DisCoCat framework. Firstly, we reformulate word prediction as a binary classification task and train quantum machine learning models on that task. Secondly, we implement a word prediction task as a multiclass classification and also train models on that. In the course of this implementation, we give a comprehensive explanation of how to design a mul- ticlass classification in the DisCoCat framework. Afterwards, we show how masking can be conceptualized inside a quantum computing environment. Then, a strategy is presented, which reduces the number of qubits and lowers the task complexity. This strategy has the potential to be extended beyond word prediction and to be applied to various other problems as well. The evaluation of the models which have been trained with the binary classification task shows promising results. The masking approach and the strategy for reducing the number of qubits were both a success in the evaluation. However, the evaluation of the models trained on the multiclass classification approach is not of the same standard as the binary approach. The reasons for that outcome are discussed in depth. For both tasks, we document the applied hyperparameters. Further research topics include the continued development of multiclass classification in the DisCoCat framework and re- search on new ansätze in quantum machine learning.
   BibTeX Entry

   @misc{mura23, author = {Jakob Murauer}, title = {{Analyzing} word predictions by quantum natural language processing}, year = {2023}, pdf = {https://bib.nm.ifi.lmu.de/pdf/mura23.pdf}, abstract = {Quantum natural language processing is an emerging field, that combines principles of quantum computing with natural language processing with the aim to enhance the capa- bilities to process and analyze human language. In classical natural language processing, word prediction is an ingredient of the pretraining task for large language models. This forms the motivation behind the exploration of a word prediction task in QNLP. This thesis deals with the implementation of a word prediction task in quantum nat- ural language processing using a mathematical framework, particularly the DisCoCat framework. Firstly, we reformulate word prediction as a binary classification task and train quantum machine learning models on that task. Secondly, we implement a word prediction task as a multiclass classification and also train models on that. In the course of this implementation, we give a comprehensive explanation of how to design a mul- ticlass classification in the DisCoCat framework. Afterwards, we show how masking can be conceptualized inside a quantum computing environment. Then, a strategy is presented, which reduces the number of qubits and lowers the task complexity. This strategy has the potential to be extended beyond word prediction and to be applied to various other problems as well. The evaluation of the models which have been trained with the binary classification task shows promising results. The masking approach and the strategy for reducing the number of qubits were both a success in the evaluation. However, the evaluation of the models trained on the multiclass classification approach is not of the same standard as the binary approach. The reasons for that outcome are discussed in depth. For both tasks, we document the applied hyperparameters. Further research topics include the continued development of multiclass classification in the DisCoCat framework and re- search on new ansätze in quantum machine learning.}, key = {mura23}, month = {7}, school = {Ludwig-Maximilians-Universität München}, supervisors = {Korbinian Staudacher and Wolfgang Gehrke (UniBw)}, type = {Masterthesis}, }