DynQ: A Dynamic Topology-Agnostic Quantum Virtual Machine via Quality-Weighted Community Detection
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917375691980800 |
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| author | Liu, Shusen Elahi, Pascal Jahan Varetto, Ugo |
| author_facet | Liu, Shusen Elahi, Pascal Jahan Varetto, Ugo |
| contents | Quantum cloud platforms have scaled hardware capacity but not the abstraction exposed to users: small programs still monopolise entire processors, and existing Quantum Virtual Machine (QVM) designs often rely on fixed, topology-specific partitions that are brittle under calibration drift, spatial heterogeneity, and transient defects.
We present DynQ, a dynamic topology-agnostic QVM that derives execution regions directly from live calibration data. DynQ models a processor as a quality-weighted coupling graph and formulates region discovery as community detection, turning high internal cohesion and low external coupling into a hardware-aware objective for quantum virtualisation. This produces regions that are compilation-friendly, quality-aware, and resilient to degraded couplers and unavailable qubits.
DynQ separates offline region discovery from online allocation, enabling low-latency scheduling over pre-validated regions while allowing recomputation under changing hardware conditions. Across five IBM backends, real-device experiments on IBM Kingston and Torino, and cross-architecture evaluation on Rigetti Ankaa-3 via AWS Braket, DynQ improves execution quality, recovers workloads lost under transient defects, and maintains stable output under concurrent batching. It reduces L1 error by up to 45.1% and improves output similarity by up to 19.1% on heterogeneous hardware, while eliminating observed baseline failures on real devices. These results position quantum virtualisation as a graph-driven systems problem and show that adaptive, quality-aware QVMs enable reliable multi-tenant quantum cloud services. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DynQ: A Dynamic Topology-Agnostic Quantum Virtual Machine via Quality-Weighted Community Detection Liu, Shusen Elahi, Pascal Jahan Varetto, Ugo Quantum Physics Software Engineering Quantum cloud platforms have scaled hardware capacity but not the abstraction exposed to users: small programs still monopolise entire processors, and existing Quantum Virtual Machine (QVM) designs often rely on fixed, topology-specific partitions that are brittle under calibration drift, spatial heterogeneity, and transient defects. We present DynQ, a dynamic topology-agnostic QVM that derives execution regions directly from live calibration data. DynQ models a processor as a quality-weighted coupling graph and formulates region discovery as community detection, turning high internal cohesion and low external coupling into a hardware-aware objective for quantum virtualisation. This produces regions that are compilation-friendly, quality-aware, and resilient to degraded couplers and unavailable qubits. DynQ separates offline region discovery from online allocation, enabling low-latency scheduling over pre-validated regions while allowing recomputation under changing hardware conditions. Across five IBM backends, real-device experiments on IBM Kingston and Torino, and cross-architecture evaluation on Rigetti Ankaa-3 via AWS Braket, DynQ improves execution quality, recovers workloads lost under transient defects, and maintains stable output under concurrent batching. It reduces L1 error by up to 45.1% and improves output similarity by up to 19.1% on heterogeneous hardware, while eliminating observed baseline failures on real devices. These results position quantum virtualisation as a graph-driven systems problem and show that adaptive, quality-aware QVMs enable reliable multi-tenant quantum cloud services. |
| title | DynQ: A Dynamic Topology-Agnostic Quantum Virtual Machine via Quality-Weighted Community Detection |
| topic | Quantum Physics Software Engineering |
| url | https://arxiv.org/abs/2601.19635 |