Paper
F. Truger, J. Barzen, M. Beisel, F. Leymann, V. Yussupov:
Warm-Starting Patterns for Quantum Algorithms – Proceedings of the 16th International Conference on Pervasive Patterns and Applications,
https://www.thinkmind.org/index.php?view=article&articleid=patterns_2024_2_10_70005
M. Stötzner, S. Becker, L. Harzenetter, F. Leymann, B. Weder, U. Breitenbücher, O. Kopp, K. Klein, J. Soldani:
A Systematic Technology Review of General-Purpose Open-Source TOSCA Orchestrators,
httpx://doi.org/10.1145/3603166.3632130
F. Truger, J. Barzen, M. Beisel, F. Leymann, and V. Yussupov:
Warm-Starting Patterns for Quantum Algorithms,
https://www.thinkmind.org/index.php?view=article&articleid=patterns_2024_2_10_70005
P. Holzer, I. Turkalj:
Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks,
arXiv:2402.14515v1 [quant-ph] 22 Feb 2024
M. Bechtold, J. Barzen, F. Leymann, A. Mandl:
Cutting a Wire with Non-Maximally Entangled States,
arXiv:2403.09690 [quant-ph], https://arxiv.org/abs/2403.09690, 13 Feb 2024
M. Ali, M. Kabel:
Piecewise Polynomial Tensor Network Quantum Feature Encodingar,
Xiv:2402.07671v2 [quant-ph] 13 Feb 2024
R. Zander, C. Becker:
Benchmarking Multipartite Entanglement Generation with Graph States,
arXiv:2402.00766v1 [quant-ph] 1 Feb 2024
C. Becker, I. Gheorghe-Pop, N. Tcholtchev:
A Testing Pipeline for Quantum Computing Applications,
2023 IEEE International Conference on Quantum Software (QSW),
https://doi.org/10.5220/001205770000353810.1109/QSW59989.2023.00016
D. Georg, J. Barzen, M. Beisel, F. Leymann, J. Obst, D. Vietz, V. Yussupov:
Execution Patterns for Quantum Applications,
Proceedings of the 18th International Conference on Software Technologies - ICSOFT,
https://doi.org/10.5220/0012057700003538
A. Wolf, C. Grozea:
Automatic Conversion of MiniZinc Programs to QUBO,
arXiv:2307.10032vl [cs.MS] 19 Jul 2023
F. Bühler, J. Barzen, M. Beisel, D. Georg, F. Leymann, K. Wild:
Patterns for Quantum Software Development,
A. Nietner, M. Ioannou, R. Sweke, R. Kueng, J. Eisert, M. Hinsche, J. Haferkamp:
On the average-case complexity of learning output distributions of quantum circuits,
arXiv:2305.05765v1 [quant-ph] 9 May 2023
J. Liu, M. Liu, J. Liu, Z. Ye, Y. Alexeev, J. Eisert, L. Jiang:
Towards provably efficient quantum algorithms for large-scale machine-learning models,
arXiv:2303.03428v2 [quant-ph] 26 Apr 2023
M. Beisel, F. Gemeinhardt, M. Salm & B. Weder:
A Practical Introduction for Developing and Operating Hybrid Quantum Applications,
Lecture Notes in Computer Science, ISSN: 1611-3349
M. Beisel, J. Barzen, M. Bechtold, F. Leymann, F. Truger, B. Weder:
QuantME4VQA: Modeling and Executing Variational Quantum Algorithms Using Workflows,
N. Pirnay , R. Sweke, J. Eisert , J. Seifert :
Superpolynomial quantum-classical separation for density modeling,
Physical Review A; https://doi.org/10.1103/PhysRevA.107.042416
J.J. Meyer, M. Mularski, E. Gil-Fuster, A.A. Mele, F. Arzani, A. Wilms, J. Eisert:
Exploiting Symmetry in Variational Quantum Machine Learning,
PRX QUANTUM 4, 010328 (2023); https://doi.org/10.1103/PRXQuantum.4.010328
M. Beisel, J. Barzen, S. Garhofer, F. Leymann, F. Truger, B. Weder, V. Yussupov:
Quokka: A Service Ecosystem for Workflow-Based Execution of Variational Quantum Algorithms,
D. Hangleiter und J. Eisert:
Computational advantage of quantum random sampling,
arXiv:2206.04079v4 [quant-ph] 10 Mar 2023
B. Weder, J. Barzen, M. Beisel, F. Leymann:
Provenance Preserving Analysis and Rewrite of Quantum Workfows for Hybrid Quantum Algorithms,
SN Computer Science (2023) 4:233 https://doi.org/10.1007/s42979-022-01625-9
J. Eisert:
A note on lower bounds to variational problems with guarantees,
arXiv:2301.06142v2 [quant-ph] 22 Jan 2023
N. Pirnay, V. Ulitzsch, F. Wilde, J. Eisert, J.P. Seifert:
A super-polynomial quantum advantage for combinatorial optimization problems,
arXiv:2212.08678v2 [quant-ph] 23 Feb 2023
M. Beisel, J. Barzen, F. Leymann, F. Truger, B. Weder, V. Yussupov:
Configurable Readout Error Mitigation in Quantum Workflows,
Electronics 2022, 11, 2983, https://doi.org/10.3390/electronics11192983
Warm-Starting Patterns for Quantum Algorithms – Proceedings of the 16th International Conference on Pervasive Patterns and Applications,
https://www.thinkmind.org/index.php?view=article&articleid=patterns_2024_2_10_70005
M. Stötzner, S. Becker, L. Harzenetter, F. Leymann, B. Weder, U. Breitenbücher, O. Kopp, K. Klein, J. Soldani:
A Systematic Technology Review of General-Purpose Open-Source TOSCA Orchestrators,
httpx://doi.org/10.1145/3603166.3632130
F. Truger, J. Barzen, M. Beisel, F. Leymann, and V. Yussupov:
Warm-Starting Patterns for Quantum Algorithms,
https://www.thinkmind.org/index.php?view=article&articleid=patterns_2024_2_10_70005
P. Holzer, I. Turkalj:
Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks,
arXiv:2402.14515v1 [quant-ph] 22 Feb 2024
M. Bechtold, J. Barzen, F. Leymann, A. Mandl:
Cutting a Wire with Non-Maximally Entangled States,
arXiv:2403.09690 [quant-ph], https://arxiv.org/abs/2403.09690, 13 Feb 2024
M. Ali, M. Kabel:
Piecewise Polynomial Tensor Network Quantum Feature Encodingar,
Xiv:2402.07671v2 [quant-ph] 13 Feb 2024
R. Zander, C. Becker:
Benchmarking Multipartite Entanglement Generation with Graph States,
arXiv:2402.00766v1 [quant-ph] 1 Feb 2024
C. Becker, I. Gheorghe-Pop, N. Tcholtchev:
A Testing Pipeline for Quantum Computing Applications,
2023 IEEE International Conference on Quantum Software (QSW),
https://doi.org/10.5220/001205770000353810.1109/QSW59989.2023.00016
Execution Patterns for Quantum Applications,
Proceedings of the 18th International Conference on Software Technologies - ICSOFT,
https://doi.org/10.5220/0012057700003538
Automatic Conversion of MiniZinc Programs to QUBO,
arXiv:2307.10032vl [cs.MS] 19 Jul 2023
Patterns for Quantum Software Development,
Proceedings of the 15th International Conference on Pervasive Patterns and Applications
(PATTERNS 2023), ISBN: 978-1-68558-049-0
On the average-case complexity of learning output distributions of quantum circuits,
arXiv:2305.05765v1 [quant-ph] 9 May 2023
Towards provably efficient quantum algorithms for large-scale machine-learning models,
arXiv:2303.03428v2 [quant-ph] 26 Apr 2023
A Practical Introduction for Developing and Operating Hybrid Quantum Applications,
Lecture Notes in Computer Science, ISSN: 1611-3349
QuantME4VQA: Modeling and Executing Variational Quantum Algorithms Using Workflows,
Proceedings of the 13th International Conference on Cloud Computing and Services Science
(CLOSER 2023), https://doi.org/10.5220/0011997500003488
Superpolynomial quantum-classical separation for density modeling,
Physical Review A; https://doi.org/10.1103/PhysRevA.107.042416
Exploiting Symmetry in Variational Quantum Machine Learning,
PRX QUANTUM 4, 010328 (2023); https://doi.org/10.1103/PRXQuantum.4.010328
Quokka: A Service Ecosystem for Workflow-Based Execution of Variational Quantum Algorithms,
Service-Oriented Computing - ICSOC 2022 Workshops
https://doi.org/10.1007/978-3-031-26507-5_35
Computational advantage of quantum random sampling,
arXiv:2206.04079v4 [quant-ph] 10 Mar 2023
Provenance Preserving Analysis and Rewrite of Quantum Workfows for Hybrid Quantum Algorithms,
SN Computer Science (2023) 4:233 https://doi.org/10.1007/s42979-022-01625-9
A note on lower bounds to variational problems with guarantees,
arXiv:2301.06142v2 [quant-ph] 22 Jan 2023
A super-polynomial quantum advantage for combinatorial optimization problems,
arXiv:2212.08678v2 [quant-ph] 23 Feb 2023
Configurable Readout Error Mitigation in Quantum Workflows,
Electronics 2022, 11, 2983, https://doi.org/10.3390/electronics11192983