Abstract: Cloud service providers face persistent challenges in allocating heterogeneous computing resources to dynamic workloads while minimising energy consumption and operational cost. Classical optimisation and machine learning techniques have delivered substantial improvements, yet they struggle with highly combinatorial allocation spaces and non-linear service-level agreements in large-scale data centre environments. This study investigates quantum-inspired optimisation approaches for cloud resource allocation, focusing on variational quantum algorithms implemented in Qiskit and hybrid quantum–classical models realised in TensorFlow Quantum. Realistic workload traces derived from a public Google cluster dataset are used to construct job arrival patterns, resource demands, and node capacity constraints, which are then encoded into quadratic unconstrained binary optimisation formulations. These formulations are solved using parameterised quantum circuits, and the resulting allocation decisions are benchmarked against mixed-integer linear programming and deep reinforcement learning baselines. Experimental results demonstrate that quantum-inspired models achieve competitive solution quality and improved robustness under volatile load conditions for medium-scale problem instances, while offering novel representational advantages for highly non-convex allocation landscapes. The proposed framework contributes to operations research and quantum computing by clarifying the practical potential and current limitations of NISQ-era techniques for cloud infrastructure management. From a managerial perspective, the findings indicate how quantum-inspired solvers can be integrated into existing optimisation pipelines as co-processors that enhance decision diversity, support scenario analysis, and inform long-term investment in quantum technologies. Keywords: quantum-inspired optimisation, cloud resource allocation, Qiskit, TensorFlow Quantum, variational quantum algorithms, NISQ computing

Abstract: Cloud service providers face persistent challenges in allocating heterogeneous computing resources to dynamic workloads while minimising energy consumption and operational cost. Classical optimisation and machine learning techniques have delivered substantial improvements, yet they struggle with highly combinatorial allocation spaces and non-linear service-level agreements in large-scale data centre environments. This study investigates quantum-inspired optimisation approaches for cloud resource allocation, focusing on variational quantum algorithms implemented in Qiskit and hybrid quantum–classical models realised in TensorFlow Quantum. Realistic workload traces derived from a public Google cluster dataset are used to construct job arrival patterns, resource demands, and node capacity constraints, which are then encoded into quadratic unconstrained binary optimisation formulations. These formulations are solved using parameterised quantum circuits, and the resulting allocation decisions are benchmarked against mixed-integer linear programming and deep reinforcement learning baselines. Experimental results demonstrate that quantum-inspired models achieve competitive solution quality and improved robustness under volatile load conditions for medium-scale problem instances, while offering novel representational advantages for highly non-convex allocation landscapes. The proposed framework contributes to operations research and quantum computing by clarifying the practical potential and current limitations of NISQ-era techniques for cloud infrastructure management. From a managerial perspective, the findings indicate how quantum-inspired solvers can be integrated into existing optimisation pipelines as co-processors that enhance decision diversity, support scenario analysis, and inform long-term investment in quantum technologies. Keywords: quantum-inspired optimisation, cloud resource allocation, Qiskit, TensorFlow Quantum, variational quantum algorithms, NISQ computing