SQUIRO: A Framework for Security-Aware Quantum-Classical Scheduling on Kubernetes
Emerging TechnologiesDistributed, Parallel, and Cluster Computing
Summary
The authors present SQUIRO, a new system that helps manage computing resources combining classical and quantum technologies while considering security needs. Their framework can work across different platforms like Kubernetes and high-performance computing by using a unified model and step-by-step design. It ensures strict security rules are followed and optimizes resource use to save cost and energy. Their tests show it works better than simpler scheduling methods, especially when dealing with regulated workloads and complex quantum hardware conditions.
Authors
Ignazio Pedone, Edoardo Giusto
Abstract
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.