A full-system energy model shows NISQ quantum simulations dominated by error mitigation sampling overhead while FTQC costs are driven by physical qubit overhead from code distance and magic states.
Referencearchitectureofaquantum-centric supercomputer.arXiv preprint arXiv:2603.10970(2026)
5 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
fields
quant-ph 5years
2026 5verdicts
UNVERDICTED 5roles
background 3polarities
background 3representative citing papers
A survey of nine QHPC stacks identifies common patterns and proposes the openQSE reference architecture to unify interfaces for interoperability in quantum-HPC environments.
Three scheduling strategies for hybrid quantum-HPC systems cut classical resource use by up to 64% or boost QPU utilization depending on workload balance, validated on real hardware.
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
A QDMI-based adapter for IQM quantum hardware enables reusable integration with Slurm and Qiskit in HPC centers, with open-source code provided.
citing papers explorer
-
Estimating The Energy Consumption of Quantum Computing from A Full System Aspect
A full-system energy model shows NISQ quantum simulations dominated by error mitigation sampling overhead while FTQC costs are driven by physical qubit overhead from code distance and magic states.
-
Quantum-HPC Software Stacks and the openQSE Reference Architecture: A Survey
A survey of nine QHPC stacks identifies common patterns and proposes the openQSE reference architecture to unify interfaces for interoperability in quantum-HPC environments.
-
Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications
Three scheduling strategies for hybrid quantum-HPC systems cut classical resource use by up to 64% or boost QPU utilization depending on workload balance, validated on real hardware.
-
Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
-
Practical HPCQC Integration with QDMI: A Real-Hardware Case Study with IQM Systems
A QDMI-based adapter for IQM quantum hardware enables reusable integration with Slurm and Qiskit in HPC centers, with open-source code provided.