Aug 2026· 2026 International Conference on Intelligent Multimedia, Networking, and Security (IMNS)· pp. 1-7· 0 citations· 22 references
Abstract
Quantum computing is rapidly moving toward cloud-native, High-Performance Computing (HPC) models. However, current job submission systems rely on sequential, exclusive-use execution, causing severe resource under-utilization and excessive user wait times. This paper introduces QUDA (Quantum Unified Device Architecture), a distributed orchestration platform designed to solve this bottleneck by decoupling the control and execution planes. QUDA employs a master-worker hierarchy in the form of the Joint Unified Gateway (JUG) for global control and distributed Quantum Processor Schedulers (QuPS) for hardware-aware execution. By abstracting hardware complexity and leveraging OpenQASM 3.0, QUDA enables backend-agnostic portability across heterogeneous systems. We evaluate QUDA using W-state probes and combinatorial optimization benchmarks, demonstrating significant gains in system-wide throughput and qubit utilization over direct submission models. QUDA provides a scalable foundation for modern, distributed quantum workloads.
The efficient and scalable integration of quantum resources into high-performance computing (HPC) environments requires standardized mechanisms for resource management, scheduling, and workflow orchestration across diverse and heterogeneous infrastructures. The Quantum Resource Management Interface (QRMI) addresses this challenge through a thin, vendor-agnostic middleware layer that provides standardized APIs for scheduling, executing, and monitoring quantum workloads while exposing quantum resources as first-class schedulable resources alongside CPUs and GPUs. Although previous work demonstrated QRMI integration with the Slurm workload manager, its applicability across other workload managers remained unexamined. This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler. We examine the integration patterns, implementation requirements, and scheduler-specific considerations associated with each environment and compare QRMI with alternative approaches to quantum resource integration. We demonstrate that QRMI provides a portable and flexible abstraction layer that minimizes scheduler-specific modifications while enabling consistent access to heterogeneous quantum resources across both on-premises and cloud environments.
Thomas Badts, T. Boyle, Claudio Carvalho et al.· arXiv.org· 3 citations
In this work, we demonstrate hybrid High Performance Computing-Quantum Computing (HPCQC) workflows on a production petascale system. The demonstration combines three components: the SuperMUC-NG supercomputer at the Leibniz Supercomputing Centre (LRZ), a 20-qubit superconducting quantum processor provided by IQM Quantum Computers (IQM), and Munich Quantum Valley (MQV)'s Munich Quantum Software Stack (MQSS). Integrating quantum processors into High Performance Computing (HPC) systems requires a heterogeneous software stack capable of orchestrating classical and quantum resources within established supercomputing workflows. MQSS treats Quantum Processing Units (QPUs) as scheduler-managed accelerators and it performs resource coordination following a two-level scheduling scheme. Slurm performs system-level allocation by exposing QPUs as Generic RESources (GRES), while the MQSS Quantum Resource Manager&Compiler Infrastructure (QRM&CI) performs just-in-time compilation and subsequent dispatch of quantum circuits. To integrate with existing HPC operations without modifying the scheduler core, MQSS introduces an open-source SLURM Plugin Suite based on Prolog/Epilog scripts and SPANK modules. Experimental results show that hybrid HPCQC workflows can be executed without significant latency overhead compared to conventional workloads. The presented architecture provides a portable integration model for quantum accelerators on large-scale HPC systems and is directly applicable to next-generation Hewlett Packard Enterprise (HPE) Cray platforms, including LRZ's upcoming'Blue Lion'supercomputer.
Muhammad Nufail Farooqi, Minh Chung, B. Mete et al.· 0 citations
Hybrid quantum-classical computing has emerged as the dominant paradigm for near-term quantum applications, yet hybrid workflows are typically described by individual algorithms rather than their underlying execution behavior. We introduce the Quantum Execution Locality Framework (QELF), a qualitative framework for characterizing hybrid quantum-classical workflows according to recurring dataflow structures and quantum execution locality, the extent to which computation remains resident on the Quantum Processing Unit (QPU) before host intervention or classical synchronization. From a representative cross-section of applications, QELF identifies five recurring computational patterns with distinct locality characteristics and discusses their implications for communication overhead, workflow organization, and future hybrid computing architectures. By providing a common vocabulary for reasoning about hybrid workloads, QELF establishes a foundation for future quantitative validation and the co-design of algorithms, runtime systems, and hybrid computing architectures.
Ryan Landfield, Jordan J. Winetrout, Michael A. Sandoval· 0 citations
Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce $\textit{Backline}$, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of $2.305~\mu$s to an AMD Ryzen Threadripper PRO CPU and $4.5~\mu$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.
Joseph K. L. Lee, M. Malekmohammadi, Hong-Sheng Zheng et al.· 0 citations
Distributed infrastructure schedulers traditionally optimise capacity, locality, and cost, but provide limited support for security posture and emerging quantum-classical workloads. As hybrid quantum-classical computing becomes increasingly practical and post-quantum security requirements begin to affect infrastructure deployment, schedulers must jointly reason about heterogeneous compute resources, security constraints, and quantum backend characteristics. We present SQUIRO, a framework for security-aware quantum-classical scheduling based on a platform-independent Unified Scheduling Model (USM) and a six-step Scheduler Design Methodology (SDM) that together enable the derivation of concrete schedulers for Kubernetes, high-performance computing (HPC), and federated environments. The framework combines multidimensional security posture enforcement through hard feasibility constraints with residual-risk optimisation, and introduces a circuit-aware quantum backend selector that accounts for coherence margin, calibration freshness, queue pressure, and hardware capabilities through a forward-compatible colocation hierarchy. Evaluation on synthetic Kubernetes clusters shows that the security model enforces complete compliance for regulated workloads by construction, while global optimisation reduces infrastructure cost by up to 51% and energy consumption by up to 63% compared with greedy placement in underloaded scenarios, without compromising admission priorities. Additional experiments characterise the solve-time growth of the current CP-SAT formulation and show that circuit-aware backend selection systematically diverges from naive error-rate ranking under coherence- and queue-limited conditions.
Quantum cloud platforms need to dynamically orchestrate workloads across heterogeneous quantum computation backends whose noise profiles, qubit topologies, and queues vary over time. Existing orchestrators use noise-agnostic heuristics that ignore backend-specific errors, causing reduced execution fidelity, load imbalance, and frequent rescheduling. To address these challenges, we propose QCOEM - a Quantum Cloud Orchestration framework that leverages Evolutionary algorithms for Multi-objective optimization of quantum task scheduling. We compare NSGA-II and NSGA-III for jointly minimizing mean completion time, execution error rate, and load imbalance. To select schedules from a non-convex Pareto front, we apply an Augmented Achievement Scalarization Function (AASF) as a preference-based decision rule that maps the Pareto set to a single dispatchable schedule aligned with user priorities. Our extensive performance evaluation in a heterogeneous quantum cloud environment shows zero task rescheduling and about 30% higher mean fidelity than noise-agnostic heuristics, while maintaining bounded scheduling overhead. The experiment results indicate that our QCOEM framework can deliver stable, high-fidelity execution and lightweight resource management for quantum cloud computing.
Tam N. Pham, H. Nguyen, Quan Le-Trung· IEEE International Conferenc...· 0 citations
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