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A Fast Quantitative Analyzer for NetKAT

Thomas Lu, Qiancheng Fu, Kevin Batz, Oliver Bøving 2026-07-18
Network Design Data × Search

The problem is that network engineers need to reason about quantitative trade-offs like bandwidth, latency, and resilience, which existing tools do not support. The method introduces a fast analyzer using weighted NetKAT (wNetKAT) and a symbolic data structure called weighted symbolic packet programs (wSPPs) to compactly represent and compute quantitative network policies. Experimental evidence shows the Rust implementation is competitive with KATch on Boolean reachability and orders of magnitude faster than McNetKAT and Storm on probabilistic analyses, with a case study on Fat-tree and Jellyfish topologies demonstrating multi-objective design-time analysis. This matters because it provides a practical, parametric framework for fast quantitative reasoning about network properties, enabling engineers to explore design trade-offs at scale.

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HybridQC: Hardware-Grounded Simulation of Tightly Integrated Hybrid Quantum-Classical Systems

Panayiotis Christou, Shuwen Kan, Ying Mao 2026-07-18
Quant Ph Hardware Simulation Communication Scheduling Runtime Workload

HybridQC addresses the problem that hybrid quantum-classical system performance is increasingly limited by classical control and communication, not quantum execution, and existing tools ignore system-topology issues like controller bottlenecks. The method introduces a topology-aware discrete-event simulator that models hybrid compute units as configurable graphs of classical and quantum devices, decomposing jobs into typed directed acyclic graphs executed under interchangeable scheduling policies. Experimental evidence shows the simulator achieves mean absolute percentage errors of 3.92%-8.04% for D-Wave QPU access time and 5.26%-19.01% for IBM quantum-seconds, and reveals that balanced 10x HCU scaling improves makespan by only 2.19x-3.42x while scheduling shifts makespan by up to 1.80x. This matters because HybridQC provides a systematic framework to evaluate topology, scheduling, and scaling limits of hybrid architectures before physical deployment, enabling researchers to identify bottlenecks and optimize resource contention.

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