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20122026
most citedQuantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

85 citations · 321 across the 25 of their papers we have counts for

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quant-ph2026

Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management

Pradeep Mantha, Florian J. Kiwit, Nishant Saurabh +2

Hybrid quantum-classical applications pose significant resource management challenges due to heterogeneity and dynamism in both infrastructure and workloads. Quantum-HPC environmen…

quant-ph2024★ 6 cited

Quantum Mini-Apps: A Framework for Developing and Benchmarking Quantum-HPC Applications

Nishant Saurabh, Pradeep Mantha, Florian J. Kiwit +2

With the increasing maturity and scale of quantum hardware and its integration into HPC systems, there is a need to develop robust techniques for developing, characterizing, and be…

quant-ph2024

QISS: Quantum Industrial Shift Scheduling Algorithm

Anna M. Krol, Marvin Erdmann, Rajesh Mishra +5

In this paper, we show the design and implementation of a quantum algorithm for industrial shift scheduling (QISS), which uses Grover's adaptive search to tackle a common and impor…

quant-ph2023★ 85 cited

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Yuri Alexeev, Maximilian Amsler, Paul Baity +124

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…

quant-ph2023★ 2 cited

A Conceptual Architecture for a Quantum-HPC Middleware

Nishant Saurabh, Shantenu Jha, Andre Luckow

Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved…

quant-ph2023★ 6 cited

Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK

Florian J. Kiwit, Marwa Marso, Philipp Ross +3

Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training t…