aiqbench analysis
HybridQC introduces a discrete-event simulator for tightly integrated hybrid quantum-classical systems, addressing critical topology-level bottlenecks in controllers, memory, and communication links that current circuit simulators miss. By leveraging real-world service profiles from D-Wave and IBM hardware, the tool enables designers to optimize scheduling policies and resource allocation before physical machines are deployed. This research provides essential infrastructure for scaling hybrid workloads efficiently as quantum-classical integration becomes a central architectural challenge.
HybridQC: Hardware-Grounded Simulation of Tightly Integrated Hybrid Quantum-Classical Systems
arXiv:2607.13352v2 Announce Type: replace-cross Abstract: Hybrid quantum-classical performance depends on how quantum processors share classical compute, controllers, memory, and communication links. Circuit simulators and provider measurements expose kernel and job behavior, but leave topology-level questions about controller bottlenecks, quantum capacity, and mixed-workload contention unanswered. We present HybridQC, a discrete-event simulator for configurable hybrid compute units (HCUs). Users specify resource graphs containing classical compute, memory, controllers, quantum annealing (QA) and digital quantum computing (DQC) devices, and links. Jobs become dependent stages for preparation, quantum execution, transfers, reconstruction, and postprocessing. Finite capacities and selectable scheduling policies determine where stages wait and overlap. Service profiles use measurements from D-Wave Advantage 1 and Advantage 2 and IBM Kingston, Marrakesh, and Fez, separating physical quantum-processor occupancy from cloud wall time. Mean absolute percentage errors are 3.92%-8.04% for D-Wave access time and 5.26%-19.01% for IBM quantum seconds. Balanced 10x HCU scaling improves makespan by 2.19x-3.42x, while policy choice changes makespan by 1.80x for a mixed workload with 20 jobs per workload family. Scaling depends on which inputs grow: 100x data growth yields median runtime near 306 s, while 100x joint growth in circuits, shots, and depth reaches 4.806 x 10^7 s on the fixed HCU. HybridQC enables designers to study topology, scheduling, and workload growth before tightly integrated quantum-classical machines exist physically.
Abstract:Hybrid quantum-classical performance depends on how quantum processors share classical compute, controllers, memory, and communication links. Circuit simulators and provider measurements expose kernel and job behavior, but leave topology-level questions about controller bottlenecks, quantum capacity, and mixed-workload contention unanswered. We present HybridQC, a discrete-event simulator for configurable hybrid compute units (HCUs). Users specify resource graphs containing classical compute, memory, controllers, quantum annealing (QA) and digital quantum computing (DQC) devices, and links. Jobs become dependent stages for preparation, quantum execution, transfers, reconstruction, and postprocessing. Finite capacities and selectable scheduling policies determine where stages wait and overlap. Service profiles use measurements from D-Wave Advantage 1 and Advantage 2 and IBM Kingston, Marrakesh, and Fez, separating physical quantum-processor occupancy from cloud wall time. Mean absolute percentage errors are 3.92%-8.04% for D-Wave access time and 5.26%-19.01% for IBM quantum seconds. Balanced 10x HCU scaling improves makespan by 2.19x-3.42x, while policy choice changes makespan by 1.80x for a mixed workload with 20 jobs per workload family. Scaling depends on which inputs grow: 100x data growth yields median runtime near 306 s, while 100x joint growth in circuits, shots, and depth reaches 4.806 x 10^7 s on the fixed HCU. HybridQC enables designers to study topology, scheduling, and workload growth before tightly integrated quantum-classical machines exist physically.
Performance (cs.PF); Quantum Physics (quant-ph)
arXiv:2607.13352 [cs.PF]
(or arXiv:2607.13352v2 [cs.PF] for this version)
https://doi.org/10.48550/arXiv.2607.13352 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 305 words