Q-PACT : Quantum-Parallel Agentic Coordination Toolkit

The convergence of quantum optimization and autonomous agentic reasoning defines a new computational frontier in large-scale resource coordination. This work introduces Q-PACT, a Quantum-Parallel Agentic Coordination Toolkit that unifies quantum optimization, fairness-driven orchestration, and adaptive multi-agent intelligence into a reproducible hybrid framework. Q-PACT addresses a key limitation in distributed and cloud-based quantum infrastructures; inefficient, inequitable scheduling of scarce quantum resources among competing agents and tasks. The proposed architecture formulates multi-agent scheduling as a Quadratic Unconstrained Binary Optimization (QUBO) model, solved through a heterogeneous pipeline integrating quantum annealing (D-Wave), variational optimization (QAOA on IonQ/Azure), and classical meta-heuristics. A noise-aware encoder, solver-adapter layer, and agentic manager collaborate to maintain fairness, adaptability, and load balance, while the QML-AURA subsystem continuously refines solver hyperparameters through feedback-driven variational learning. Q-PACT’s hybrid orchestration layer enables dynamic re-optimization, fault-tolerant fallback execution, and secure artifact exchange across distributed backends, achieving resilience under the noise and queueing constraints of NISQ-era hardware. Empirical evaluations demonstrate significant improvements in makespan reduction, workload variance, and energy minimization compared with classical heuristics, establishing the viability of quantum-assisted agentic scheduling at scale. By bridging quantum optimization paradigms, multi-agent systems, and fairness-aware orchestration, Q-PACT provides a foundational blueprint for the next generation of autonomous, cloud-integrated, and quantum-enabled coordination systems.