quantum cloud operators

Abstract: Multi-tenant computing enables providers of commercial cloud quantum processors to rent disjoint sectors of a device to independent users. Average gate error, which cloud quantum computing providers report, does not determine how much one tenant learns about another. We propose an information-theoretic notion of information leakage across co-tenancy boundaries stemming from quantum state distinguishability. We measure this leakage on commercially-available 156-qubit (IBM Kingston) and 20-qubit (IQM Garnet) devices. Boundaries with identical benchmarked error can offer significantly different amount of information leakage because standard reported measures of error are blind to coherent-versus-stochastic nature of the error while the proposed notion of information leakage is not. A uniform Pauli randomisation implemented over the victim's whole register is used as a defence mechanism to reduce the information leakage to zero. The defence theoretically does not incur a fidelity cost, but the experiments show a non-trivial degradation caused by accumulation of errors. We provide a specific call-for-action to the providers of quantum cloud computing to report information leakage in addition to standard error rates in their device datasheet to enable users to compute privacy and security risks prior to engagement with the device.

Abstract: Quantum cloud computing, delivered through the quantum-as-a-service (QaaS) model, provides access to quantum computing resources. However, applying uniform time-based pricing across fundamentally heterogeneous quantum resources significantly complicates task orchestration, particularly when addressing the tradeoff between execution costs and system performance. While heuristic methods rely on predefined scheduling rules, classical deep reinforcement learning (DRL) models may require more trainable parameters in this setting. Motivated by the potential of parameterised quantum circuits (PQCs) as compact function approximators, we propose QRLQ, a cost-delay-aware quantum cloud scheduling framework integrating PQCs with a dueling double deep Q-network (D3QN) to dynamically account for both cost and delay. Our simulation results show that QRLQ achieves lower mean cost and delay than the heuristic baselines, achieving a 5-11% lower mean cost relative to availability-based and rotation-based heuristics and reducing mean delay by 17% and 82% relative to the strongest and weakest heuristic baselines, respectively, while retaining execution fidelity within 2% of a fidelity-greedy policy. Compared with the classical DRL baseline, QRLQ achieves comparable scheduling performance while using 72% fewer trainable parameters. This work explores the feasibility of using QRL for task orchestration in quantum cloud environments and demonstrates its potential for cost-delay-aware quantum resource management.