Quantum broker manages job distribution across quantum clouds
Quantum Broker: QoS-Aware Distribution of Quantum Computations
Emerging TechnologiesSoftware Engineering
Summary
Quantum computers come in many types and qualities, and when using them through the cloud, it's tricky to send work in a way that keeps costs low, results reliable, and waits short. The authors built the Quantum Broker, a tool that spreads pieces of a quantum task over different machines in clever ways based on user priorities like speed or accuracy. It uses special solvers to decide the best way to divide the work and has a simple language to say what kind of quality is needed. Their tests show it works better than just picking one machine or randomly splitting jobs.
What this means in practice
- •For quantum software developers: Manage execution of quantum programs across multiple cloud quantum computers to meet custom quality and cost goals.
- •For cloud infrastructure operators: Optimize use and pricing of diverse quantum hardware by coordinating workload distribution dynamically based on client needs.
Authors
Giuseppe Bisicchia, Alessandro Bocci, Jose Garcia-Alonso, Juan Manuel Murillo, Antonio Brogi
Abstract
Cloud-accessible quantum computing has made heterogeneous Quantum Processing Units (QPUs) available through provider platforms that differ in qubit technology, calibration state, queueing behavior, and pricing model. This heterogeneity makes execution management a quantum-engineering concern: a quantum circuit may be executable on several backends, but the resulting cost, latency, fidelity, and reproducibility depend on backend selection and on how measurement shots are allocated. This paper presents the Quantum Broker, an open-source and provider-agnostic framework for Quality-of-Service-aware quantum execution management. The framework operationalizes shot-wise distribution by exploiting the statistical independence of quantum measurement shots to dispatch a single circuit across multiple heterogeneous QPUs according to user-defined constraints and optimization objectives. It combines a modular execution architecture, interchangeable backend-profiling strategies, and the Quantum QoS Specification Language (QQSpec), a declarative language for expressing QoS requirements. Dispatch planning is supported by two solver backends: a mixed-integer linear-programming (MILP) solver, and a nonlinear dual-annealing solver. Experiments on benchmark quantum circuits and noisy simulator backends show that the Quantum Broker can produce feasible QoS-aware dispatch plans under heterogeneous backend conditions and can improve QoS satisfaction with respect to single-backend and naive multi-backend baselines. The results also expose a practical trade-off between planning time and dispatch quality, with the nonlinear solver producing higher-quality allocations at increased computational cost. Overall, the Quantum Broker provides an extensible execution-management layer for engineering portable, reproducible, and QoS-aware quantum software over fragmented quantum cloud infrastructures.