EniQmA – Publikationen

M. Bechtold, J. Barzen, F. Leymann, A. Mandl, J. Obst, F. Truger, B. Weder:
Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
Quantum Science Technology 8
https://iopscience.iop.org/article/10.1088/2058-9565/acf59c

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

M. Ali, M. Kabel:
Performance Study of Variational Quantum Algorithms for Solving the Poisson Equation on a Quantum Computer
https://doi.org/10.1103/PhysRevApplied.20.014054D. Georg, J. Barzen, M. Beisel, B. Weder, 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(PATTERNS 2023), ISBN: 978-1-68558-049-0

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