Publikationen

Hier finden Sie eine nicht vollständige Liste der MQV-Veröffentlichungen:

  • S. Adarsh et al. SyReC Synthesizer: An MQT tool for synthesis of reversible circuits. en. In: Software Impacts 14 (Dec. 2022), p. 100451. issn: 26659638.
  • P. Adelhardt and K. P. Schmidt. Continuously varying critical exponents in long-range quantum spin ladders. en. In: SciPost Physics 15.3 (Sept. 2023), p. 087. issn: 2542-4653.
  • K. Adhikari and C. Deppe. Quantum Information Spreading and Scrambling in a Distributed Quantum Network. In: European Wireless 2023. IEEE, Oct. 2023.
  • K. Adhikari and C. Deppe. Quantum information spreading and scrambling in a distributed quantum network: A Hasse/Lamport diagrammatic approach. arXiv:2309.10363 [cond-mat, physics:quant-ph]. Sept. 2023.
  • K. Adhikari et al. Krylov Complexity of Fermionic and Bosonic Gaussian States. arXiv:2309.10382 [cond-mat, physics:hep-th, physics:quant-ph]. Sept. 2023.
  • P. Altmann et al. SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training: in: Proceedings of the 15th International Conference on Agents and Artificial Intelligence. Lisbon, Portugal: SCITEPRESS - Science and Technology Publications, 2023, pp. 744–751. isbn: 978-989-758-623-1.
  • Z. Amiri, B. A. Bash, and J. Nötzel. Performance of Quantum Preprocessing under Phase Noise. In: 2022 IEEE Globecom Workshops (GC Wkshps). Dec. 2022, pp. 298–303.
  • M. Artner, G. Wallner, and R. Wille. Introducing QRogue: Teaching Quantum Computing Using a Rogue-like Game Concept. en. In: Proceedings of the 18th International Conference on the Foundations of Digital Games. Lisbon Portugal: ACM, Apr. 2023, pp. 1–4. isbn: 978-1-4503-9855-8.
  • N. Astrakhantsev et al. Time evolution of uniform sequential circuits. In: Physical Review Research 5.3 (Sept. 2023). Publisher: American Physical Society, p. 033187.
  • A. Bacho, H. Boche, and G. Kutyniok. Complexity Blowup for Solutions of the Laplace and the Diffusion Equation. arXiv:2212.00693 [cs, math]. Sept. 2023.
  • A. Bacho, H. Boche, and G. Kutyniok. Reliable AI: Does the Next Generation Require Quantum Computing? arXiv:2307.01301 [quant-ph]. July 2023.
  • J. Bender, P. Emonts, and J. I. Cirac. A variational Monte Carlo algorithm for lattice gauge theories with continuous gauge groups: a study of (2+1)-dimensional compact QED with dynamical fermions at finite density. arXiv:2304.05916 [cond-mat, physics:hep-lat, physics:quant-ph]. Apr. 2023.
  • A. Bentellis et al. Benchmarking the Variational Quantum Eigensolver using different quantum hardware. In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE). Vol. 01. Sept. 2023, pp. 518–523.
  • L. Berent, L. Burgholzer, and R. Wille. Software Tools for Decoding Quantum Low-Density Parity-Check Codes. In: Proceedings of the 28th Asia and South Pacific Design Automation Conference. ASPDAC ’23. New York, NY, USA: Association for Computing Machinery, Jan. 2023, pp. 709–714. isbn: 978-1-4503-9783-4.
  • L. Berent, L. Burgholzer, and R. Wille. Towards a SAT encoding for quantum circuits: A journey from classical circuits to clifford circuits and beyond. In: 25th international conference on theory and applications of satisfiability testing (SAT 2022). Ed. by K. S. Meel and O. Strichman. Vol. 236. Leibniz international proceedings in informatics (LIPIcs). ISSN: 1868-8969 tex.urn: urn:nbn:de:0030-drops-166927. Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum fu ̈r Informatik, 2022, 18:1–18:17. isbn: 978-3-95977-242-6.
  • S. Birnkammer, A. Bastianello, and M. Knap. Prethermalization in one-dimensional quantum many-body systems with confinement. In: Nature Communications 13.1 (Dec. 2022). arXiv:2202.12908 [cond-mat, physics:hep-th, physics:quant-ph], p. 7663. issn: 2041-1723.
  • H. Boche, A. Fono, and G. Kutyniok. Limitations of Deep Learning for Inverse Problems on Digital Hardware. arXiv:2202.13490 [cs, eess]. Oct. 2022.
  • H. Boche, A. Fono, and G. Kutyniok. Non-Computability of the Pseudoinverse on Digital Computers. arXiv:2212.02940 [cs, math]. Dec. 2022.
  • L. Burgholzer and R. Wille. Exploiting Reversible Computing for Verification: Potential, Possible Paths, and Consequences. In: 2023.
  • L. Burgholzer, A. Ploier, and R. Wille. Exploiting Arbitrary Paths for the Simulation of Quantum Circuits with Decision Diagrams. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). Antwerp, Belgium: IEEE, Mar. 2022, pp. 64–67. isbn: 978-3-9819263-6-1.
  • L. Burgholzer, A. Ploier, and R. Wille. Simulation Paths for Quantum Circuit Simulation with Decision Diagrams. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2022), pp. 1–1. issn: 0278-0070, 1937-4151.
  • L. Burgholzer, S. Schneider, and R. Wille. Limiting the Search Space in Optimal Quantum Circuit Mapping. In: 2022 27th Asia and South Pacific Design Automation Conference (ASPDAC). Taipei, Taiwan: IEEE, Jan. 2022, pp. 466–471. isbn: 978-1-66542-135-5.
  • L. Burgholzer and R. Wille. Advanced Equivalence Checking for Quantum Circuits. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 40.9 (Sept. 2021), pp. 1810–1824. issn: 0278-0070, 1937-4151.
  • L. Burgholzer and R. Wille. Exploiting Reversible Computing for Verification: Potential, Possible Paths, and Consequences. In: Proceedings of the 28th Asia and South Pacific Design Automation Conference. ASPDAC ’23. New York, NY, USA: Association for Computing Machinery, Jan. 2023, pp. 429–435. isbn: 978-1-4503-9783-4.
  • L. Burgholzer and R. Wille. Handling non-unitaries in quantum circuit equivalence checking. en. In: Proceedings of the 59th ACM/IEEE Design Automation Conference. San Francisco California: ACM, July 2022, pp. 529–534. isbn: 978-1-4503-9142-9.
  • L. Burgholzer, R. Wille, and R. Kueng. Characteristics of reversible circuits for error detection. en. In: Array 14 (July 2022), p. 100165. issn: 25900056.
  • Y. Chen and Y. Stade. Artifact for Quantum Constant Propagation. In: (May 2023).
  • Y. Chen and Y. Stade. Quantum Constant Propagation. en. In: Static Analysis. Ed. by M. V. Hermenegildo and J. F. Morales. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2023, pp. 164–189. isbn: 978-3-031-44245-2.
  • S. Dehdashti, J. Notzel, and P. van Loock. Quantum capacity of a deformed bosonic dephasing channel. arXiv:2211.09012 [math-ph, physics:physics, physics:quant-ph]. Nov. 2022.
  • J. Denzler et al. Learning fermionic correlations by evolving with random translationally invariant Hamiltonians. 2023. arXiv: 2309.12933 [quant-ph].
  • R. Dilip et al. Data compression for quantum machine learning. In: Physical Review Research 4.4 (Oct. 2022). Publisher: American Physical Society, p. 043007.
  • L. Ding, G. Du ̈nnweber, and C. Schilling. Physical Entanglement Between Localized Orbitals. arXiv:2303.14170 [cond-mat, physics:physics, physics:quant-ph]. Mar. 2023.
  • L. Ding et al. Quantum correlations in molecules: from quantum resourcing to chemical bonding. en. In: Quantum Science and Technology 8.1 (Dec. 2022). Publisher: IOP Publishing, p. 015015. issn: 2058-9565.
  • T.-A. Dragan et al. Quantum Reinforcement Learning for Solving a Stochastic Frozen Lake Environment and the Impact of Quantum Architecture Choices: in: Proceedings of the 15th International Conference on Agents and Artificial Intelligence. Lisbon, Portugal: SCITEPRESS - Science and Technology Publications, 2023, pp. 199–210. isbn: 978-989-758-623-1.
  • T. Eckstein et al. Large-scale simulations of Floquet physics on near-term quantum computers. arXiv:2303.02209 [cond-mat, physics:quant-ph]. Mar. 2023.
  • A. Elsharkawy et al. Integration of Quantum Accelerators with High Performance Computing – A Review of Quantum Programming Tools. arXiv:2309.06167 [quant-ph]. Sept. 2023.
  • F. vom Ende. Quantum-Dynamical Semigroups and the Church of the Larger Hilbert Space. In: Open Systems & Information Dynamics 30.01 (Mar. 2023). Publisher: World Scientific Publishing Co., p. 2350003. issn: 1230-1612.
  • F. vom Ende. Which bath Hamiltonians matter for thermal operations? In: Journal of Mathematical Physics 63.11 (Nov. 2022), p. 112202. issn: 0022-2488.
  • F. vom Ende and G. Dirr. The d-Majorization Polytope. en. In: Linear Algebra and its Applications 649 (Sept. 2022), pp. 152–185. issn: 0024-3795.
  • F. vom Ende et al. Exploring the Limits of Controlled Markovian Quantum Dynamics with Thermal Resources. In: Open Systems & Information Dynamics 30.01 (Mar. 2023). Publisher: World Scientific Publishing Co., p. 2350005. issn: 1230-1612.
  • F. v. Ende and E. Malvetti. The Thermomajorization Polytope and Its Degeneracies. arXiv:2212.04305 [math-ph, physics:quant-ph]. Dec. 2022.
  • F. v. Ende et al. Exploring the Limits of Controlled Markovian Quantum Dynamics with Thermal Resources I+II. In: 25th International Symposium on Mathematical Theory of Networks and Systems. Bayreuth, 2022, pp. 1069–72, 1073–1076.
  • M. Fischer et al. Combining experiments on luminescent centres in hexagonal boron nitride with the polaron model and ab initio methods towards the identification of their microscopic origin. 2023. arXiv: 2209.08910 [cond-mat.mtrl-sci].
  • T. Gabor, M. Zorn, and C. Linnhoff-Popien. The Applicability of Reinforcement Learning for the Automatic Generation of State Preparation Circuits. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO ’22. Boston, Massachusetts: Association for Computing Machinery, 2022, pp. 2196–2204. isbn: 9781450392686.
  • V. Galetsky et al. Comparison of Quantum PUF models. In: 2022 IEEE Globecom Workshops (GC Wkshps). Dec. 2022, pp. 820–825.
  • A. Gleis, J.-W. Li, and J. von Delft. Controlled Bond Expansion for Density Matrix Renormalization Group Ground State Search at Single-Site Costs. In: Physical Review Letters 130.24 (June 2023). Publisher: American Physical Society, p. 246402.
  • A. Gleis, J.-W. Li, and J. von Delft. Projector formalism for kept and discarded spaces of matrix product states. In: Physical Review B 106.19 (Nov. 2022). Publisher: American Physical Society, p. 195138.
  • Z. Gong, T. Guaita, and J. I. Cirac. Long-Range Free Fermions: Lieb-Robinson Bound, Clustering Properties, and Topological Phases. In: Physical Review Letters 130.7 (Feb. 2023). arXiv:2210.05389 [cond-mat, physics:math-ph, physics:quant-ph], p. 070401. issn: 0031-9007, 1079-7114.
  • G. Gonzalez-Garc ́ıa, R. Trivedi, and J. I. Cirac. Error Propagation in NISQ Devices for Solving Classical Optimization Problems. In: PRX Quantum 3.4 (Dec. 2022). Publisher: American Physical Society, p. 040326.
  • J. Gröbmeyer et al. Space-charge limited and ultrafast dynamics in graphene-based nano-gaps. In: Applied Physics Letters 123.1 (July 2023), p. 013504. issn: 0003-6951. eprint: pubs.aip.org/aip/apl/article-pdf/doi/10.1063/5.0154152/18147013/ 013504\_1\_5.0154152.pdf.
  • T. Grurl, J. Fub, and R. Wille. Noise-aware Quantum Circuit Simulation With Decision Diagrams. In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2022), pp. 1–1. issn: 0278-0070, 1937-4151.
  • T. Grurl, J. Fuß, and R. Wille. Optimized Density Matrix Representations : Improving the Basis for Noise-Aware Quantum Circuit Design Tools. In: 2023 IEEE 53rd International Symposium on Multiple-Valued Logic (ISMVL). Matsue, Japan: IEEE, May 2023, pp. 141– 146. isbn: 978-1-66546-416-1.
  • T. Grurl et al. Automatic Implementation and Evaluation of Error-Correcting Codes for Quantum Computing: An Open-Source Framework for Quantum Error Correction. In: 2023 36th International Conference on VLSI Design and 2023 22nd International Conference on Embedded Systems (VLSID). Hyderabad, India: IEEE, Jan. 2023, pp. 301–306. isbn: 9798350346787.
  • X. Guo, K. Qin, and M. Schulz. HiSEP-Q: A Highly Scalable and Efficient Quantum Control Processor for Superconducting Qubits. In: arXiv preprint arXiv:2308.16776, to appear in ICCD 2023 (2023).
  • X. Guo and M. Schulz. A Scalable and Cross-Technology Quantum Control Processor. In: 2023 33rd International Conference on Field-Programmable Logic and Applications (FPL). 2023, pp. 353–354.
  • M. Haider, Y. Yuan, and C. Jirauschek. A Quantum Model of a Dissipative-Dispersive Josephson Traveling-Wave Parametric Amplifier Including Impedance-Mismatch-Induced Reflections. In: Aug. 2023, pp. 1–4.
  • M. Haider, Y. Yuan, and C. Jirauschek. Nonlinear Elements in Traveling-Wave Parametric Amplifiers for Dispersive Qubit Readout. In: 2023 17th European Conference on Antennas and Propagation (EuCAP). 2023, pp. 1–5.
  • M. Haider et al. Quantum Models for Flux-Driven Superconducting Traveling-Wave Parametric Amplifiers with Different Nonlinear Junction Topologies. In: 2023 IEEE/MTT-S International Microwave Symposium - IMS 2023. 2023, pp. 664–667.
  • L. Haller et al. Quantum phase transition between symmetry enriched topological phases in tensor-network states. arXiv:2305.02432 [cond-mat, physics:quant-ph]. Sept. 2023.
  • D. Hangleiter and J. Eisert. Computational advantage of quantum random sampling. In: Rev. Mod. Phys. 95 (3 July 2023), p. 035001.
  • J. Helsen et al. General Framework for Randomized Benchmarking. In: PRX Quantum 3.2 (June 2022). Publisher: American Physical Society, p. 020357.
  • J. Helsen et al. Shadow estimation of gate-set properties from random sequences. en. In: Nature Communications 14.1 (Aug. 2023). Number: 1 Publisher: Nature Publishing Group, p. 5039. issn: 2041-1723.
  • J. Herrmann et al. Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases. en. In: Nature Communications 13.1 (July 2022). Number: 1 Publisher: Nature Publishing Group, p. 4144. issn: 2041-1723.
  • L. Heunisch, C. Eichler, and M. J. Hartmann. Tunable coupler to fully decouple superconducting qubits. In: (June 2023). arXiv: 2306.17007 [quant-ph].
  • S. Heußen et al. Strategies for a practical advantage of fault-tolerant circuit design in noisy trapped-ion quantum computers. In: Phys. Rev. A 107 (4 Apr. 2023), p. 042422.
  • S. Hillmich and R. Wille. Efficient Implementation of Quantum Circuit Simulation with Decision Diagrams. Springer, 2023.
  • S. Hillmich et al. Approximating Decision Diagrams for Quantum Circuit Simulation. en. In: ACM Transactions on Quantum Computing 3.4 (Dec. 2022), pp. 1–21. issn: 2643-6809, 2643-6817.
  • S. Hillmich et al. Reordering Decision Diagrams for Quantum Computing Is Harder Than You Might Think. en. In: Reversible Computation. Ed. by C. A. Mezzina and K. Podlaski. Vol. 13354. Series Title: Lecture Notes in Computer Science. Cham: Springer International Publishing, 2022, pp. 93–107. isbn: 978-3-031-09004-2 978-3-031-09005-9.
  • M. Hinsche et al. One T Gate Makes Distribution Learning Hard. In: Phys. Rev. Lett. 130 (24 June 2023), p. 240602.
  • T. O. Höhn et al. State-dependent potentials for the 1S0 and 3P0 clock states of neutral ytterbium atoms. In: Phys. Rev. A 108 (5 Nov. 2023), p. 053325.
  • C.-L. Hong et al. Quantum Parallelized Variational Quantum Eigensolvers for Excited States. arXiv:2306.11844 [cond-mat, physics:physics, physics:quant-ph]. June 2023.
  • M. HörmannandK.P.Schmidt.Projectivecluster-additivetransformationforquantum lattice models. en. In: SciPost Physics 15.3 (Sept. 2023), p. 097. issn: 2542-4653.
  • A. Hötger et al. Spin-defect characteristics of single sulfur vacancies in monolayer MoS2. In: 2D Mater Appl 7.30 (Apr. 2023).
  • H.-K. Jin, J. Knolle, and M. Knap. Fractionalized Prethermalization in a Driven Quantum Spin Liquid. In: Physical Review Letters 130.22 (June 2023). Publisher: American Physical Society, p. 226701.
  • H.-K. Jin et al. Kinetic Ferromagnetism and Topological Magnons of the Hole-Doped Kitaev Spin Liquid. arXiv:2309.15153 [cond-mat]. Sept. 2023.
  • D. T. Jonas Winklmann and M. Schulz. Realistic Neutral Atom Image Simulation. In: Proceedings of IEEE QCE 2023. 2023.
  • W. Kadow, F. Pollmann, and M. Knap. Isometric tensor network representations of two-dimensional thermal states. In: Physical Review B 107.20 (May 2023). Publisher: American Physical Society, p. 205106.
  • W. Kadow, L. Vanderstraeten, and M. Knap. Hole Spectral Function of a Chiral Spin Liquid in the Triangular Lattice Hubbard Model. In: Physical Review B 106.9 (Sept. 2022). arXiv:2202.03458 [cond-mat, physics:quant-ph], p. 094417. issn: 2469-9950, 2469-9969.
  • W. Kadow et al. Single-hole spectra of Kitaev spin liquids: From dynamical Nagaoka ferro-magnetism to spin-hole fractionalization. arXiv:2309.15157 [cond-mat]. Sept. 2023.
  • M. Kaul, A. Ku ̈chler, and C. Banse. A Uniform Representation of Classical and Quantum Source Code for Static Code Analysis. In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE). Vol. 01. Sept. 2023, pp. 1013–1019.
  • U. E. Khodaeva, D. L. Kovrizhin, and J. Knolle. Quantum simulation of the 1D Fermi– Hubbard model as a Z2 lattice-gauge theory. arXiv:2305.04648 [cond-mat, physics:quant-ph]. May 2023.
  • J. Knörzer, D. Malz, and J. I. Cirac. Cross-platform verification in quantum networks. In: Physical Review A 107.6 (June 2023). Publisher: American Physical Society, p. 062424.
  • M. Kölleetal.DisentanglingQuantumandClassicalContributionsinHybridQuantum Machine Learning Architectures. In: arXiv preprint arXiv:2311.05559 (2023).
  • A. Kotil et al. Riemannian quantum circuit optimization for Hamiltonian simulation. In: arXiv:2212.07556 (2023). eprint: 2212.07556.
  • J. A. Koziol et al. Systematic analysis of crystalline phases in bosonic lattice models with algebraically decaying density-density interactions. en. In: SciPost Physics 14.5 (May 2023), p. 136. issn: 2542-4653.
  • F. Kranzl et al. Observation of Magnon Bound States in the Long-Range, Anisotropic Heisenberg Model. In: Physical Review X 13.3 (Aug. 2023). Publisher: American Physical Society, p. 031017.
  • M. Krenn et al. Artificial intelligence and machine learning for quantum technologies. In: Physical Review A 107.1 (Jan. 2023). Publisher: American Physical Society, p. 010101.
  • C. Kuhlenkamp et al. Tunable topological order of pseudo spins in semiconductor heterostructures. arXiv:2209.05506 [cond-mat, physics:quant-ph]. Sept. 2022.
  • S. Lang et al. Aluminum Josephson Junction Formation on 200mm Wafers Using Different Oxidation Techniques. In: ECS Transactions 111.1 (May 2023), p. 41.
  • J. Lee et al. Quantum Task Offloading with the OpenMP API. In: Poster Proceedings of SC23. Nov. 2023.
  • Y. Lee, H. Boche, and G. Kutyniok. Computability of Optimizers. arXiv:2301.06148 [cs, math]. Jan. 2023.
  • L. Lenke, A. Schellenberger, and K. P. Schmidt. Series expansions in closed and open quantum many-body systems with multiple quasiparticle types. In: Physical Review A 108.1 (July 2023). Publisher: American Physical Society, p. 013323.
  • J.-W. Li, A. Gleis, and J. von Delft. Time-dependent variational principle with controlled bond expansion for matrix product states. arXiv:2208.10972 [cond-mat, physics:quant-ph]. Aug. 2022.
  • J. Liebert, A. Y. Chaou, and C. Schilling. Refining and relating fundamentals of functional theory. In: The Journal of Chemical Physics 158.21 (June 2023), p. 214108. issn: 0021-9606.
  • J. Liebert and C. Schilling. Deriving density-matrix functionals for excited states. en. In: SciPost Physics 14.5 (May 2023), p. 120. issn: 2542-4653.
  • S.-H. Lin, M. P. Zaletel, and F. Pollmann. Efficient simulation of dynamics in two-dimensional quantum spin systems with isometric tensor networks. In: Physical Review B 106.24 (Dec. 2022). Publisher: American Physical Society, p. 245102.
  • Y.-J. Liu et al. Methods for Simulating String-Net States and Anyons on a Digital Quantum Computer. In: PRX Quantum 3.4 (Nov. 2022). Publisher: American Physical Society, p. 040315.
  • Y.-J. Liu et al. Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks. In: Physical Review Letters 130.22 (June 2023). Publisher: American Physical Society, p. 220603.
  • E. Malvetti et al. Analytic, Differentiable and Measurable Diagonalizations in Symmetric Lie Algebras. arXiv:2212.00713 [math]. June 2023.
  • E. Malvetti et al. Reachability, Coolability, and Stabilizability of Open Markovian Quantum Systems with Fast Unitary Control. arXiv:2308.00561 [quant-ph]. Aug. 2023.
  • D. Malz and J. I. Cirac. Few-Body Analog Quantum Simulation with Rydberg-Dressed Atoms in Optical Lattices. In: PRX Quantum 4.2 (Apr. 2023). Publisher: American Physical Society, p. 020301.
  • D. Malz et al. Preparation of matrix product states with log-depth quantum circuits. arXiv:2307.01696 [cond-mat, physics:quant-ph]. July 2023.
  • R. Mansuroglu, F. Fischer, and M. J. Hartmann. Problem specific classical optimization of Hamiltonian simulation. In: Physical Review Research 5.4 (Oct. 2023). arXiv:2306.07208 [quant-ph], p. 043035. issn: 2643-1564.
  • R. Mansuroglu et al. Variational Hamiltonian simulation for translational invariant systems via classical pre-processing. In: Quantum Science and Technology 8.2 (Jan. 2023), p. 025006.
  • K. Mato, S. Hillmich, and R. Wille. Compression of Qubit Circuits: Mapping to Mixed-Dimensional Quantum Systems. In: 2023 IEEE International Conference on Quantum Software (QSW). Chicago, IL, USA: IEEE, July 2023, pp. 155–161. isbn: 9798350304794.
  • K. Mato, S. Hillmich, and R. Wille. Mixed-Dimensional Quantum Circuit Simulation with Decision Diagrams. In: 2023 IEEE International Conference on Quantum Computing and Engineering (QCE). Vol. 01. Sept. 2023, pp. 978–989.
  • K. Mato et al. Adaptive Compilation of Multi-Level Quantum Operations. In: 2022 IEEE International Conference on Quantum Computing and Engineering (QCE). arXiv:2206.03842 [quant-ph]. Sept. 2022, pp. 484–491.
  • K. Mato et al. Compilation of Entangling Gates for High-Dimensional Quantum Systems. In: Proceedings of the 28th Asia and South Pacific Design Automation Conference. ASPDAC ’23. New York, NY, USA: Association for Computing Machinery, Jan. 2023, pp. 202–208. isbn: 978-1-4503-9783-4.
  • B. Mete, M. Schulz, and M. Ruefenacht. Predicting the Optimizability for Workflow Decisions. In: 2022 IEEE/ACM Third International Workshop on Quantum Computing Software (QCS). Dallas, TX, USA: IEEE, Nov. 2022, pp. 68–74. isbn: 978-1-66547-536-5.
  • J. J. Meyer et al. Exploiting Symmetry in Variational Quantum Machine Learning. In: PRX Quantum 4 (1 Mar. 2023), p. 010328.
  • N. Meyer et al. Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning. arXiv:2304.13571 [quant-ph]. Aug. 2023.
  • N. Meyer et al. Quantum Policy Gradient Algorithm with Optimized Action Decoding. arXiv:2212.06663 [quant-ph]. May 2023.
  • A. Micevic et al. On-demand generation of optically active defects in monolayer WS2 by a focused helium ion beam. In: Applied Physics Letters 121.18 (Nov. 2022), p. 183101. issn: 0003-6951.
  • R. M. Milbradt et al. Ternary Unitary Quantum Lattice Models and Circuits in 2 + 1 Dimensions. In: Phys. Rev. Lett. 130 (9 Mar. 2023), p. 090601.
  • N. Mohseni et al. Deep learning of many-body observables and quantum information scrambling. arXiv:2302.04621 [quant-ph]. Feb. 2023.
  • R. Morral-Yepes, F. Pollmann, and I. Lovas. Detecting and stabilizing measurement-induced symmetry-protected topological phases in generalized cluster models. arXiv:2302.14551 [cond-mat, physics:quant-ph]. Feb. 2023.
  • R. Morral-Yepes et al. Entanglement Transitions in Unitary Circuit Games. arXiv:2304.12965 [cond-mat, physics:quant-ph]. Apr. 2023.
  • M. Mu ̈hlhauser and K. P. Schmidt. Linked cluster expansions via hypergraph decompositions. In: Physical Review E 105.6 (June 2022). Publisher: American Physical Society, p. 064110.
  • M. Na ̈gele and F. Marquardt. Optimizing ZX-Diagrams with Deep Reinforcement Learning. In: (Nov. 2023). arXiv:2311.18588 [quant-ph].
  • K. Nisi et al. Defect-Engineered Magnetic Field Dependent Optoelectronics of Vanadium Doped Tungsten Diselenide Monolayers. In: Advanced Optical Materials 10 (June 2022).
  • J. Nötzel and M. Rosati. Operating Fiber Networks in the Quantum Limit. In: Journal of Lightwave Technology (2023). Conference Name: Journal of Lightwave Technology, pp. 1–11. issn: 1558-2213.
  • J. Old and M. Rispler. Generalized Belief Propagation Algorithms for Decoding of Surface Codes. In: Quantum 7 (June 2023), p. 1037. issn: 2521-327X.
  • M. Oliv et al. Evaluating the impact of noise on the performance of the Variational Quantum Eigensolver. In: (2022).
  • J. Olle et al. Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent. arXiv:2311.04750 [quant-ph]. Nov. 2023.
  • E. Onorati, T. Kohler, and T. S. Cubitt. Fitting time-dependent Markovian dynamics to noisy quantum channels. arXiv:2303.08936 [quant-ph]. Mar. 2023.
  • E. Onorati et al. Efficient learning of ground & thermal states within phases of matter. arXiv:2301.12946 [math-ph, physics:quant-ph]. May 2023.
  • T. Peham, L. Burgholzer, and R. Wille. On Optimal Subarchitectures for Quantum Circuit Mapping. In: (2023).
  • T. Peham et al. Depth-Optimal Synthesis of Clifford Circuits with SAT Solvers. In: Proceedings of IEEE QCE 2023. 2023.
  • T. Peham, L. Burgholzer, and R. Wille. Equivalence Checking of Parameterized Quantum Circuits: Verifying the Compilation of Variational Quantum Algorithms. In: Proceedings of the 28th Asia and South Pacific Design Automation Conference. ASPDAC ’23. New York, NY, USA: Association for Computing Machinery, Jan. 2023, pp. 702–708. isbn: 978-1-4503-9783-4.
  • T. Peham, L. Burgholzer, and R. Wille. Equivalence Checking of Quantum Circuits with the ZX-Calculus. In: IEEE Journal on Emerging and Selected Topics in Circuits and Systems 12.3 (Sept. 2022). arXiv:2208.12820 [quant-ph], pp. 662–675. issn: 2156-3357, 2156-3365.
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