Post-cut metadata from quantum circuit fragments enables high-accuracy inference of algorithm family, cut mechanism, and Hamiltonian structure via machine learning on fragment width, depth, and gate counts.
Circuit knitting with classical communication
6 Pith papers cite this work. Polarity classification is still indexing.
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A weak-coupling approximation reduces classical overhead in quantum circuit knitting to polynomial cost when one qubit couples weakly to others, shown on QAOA-style layered circuits.
InterQ presents a communication-aware scheduler for modular QPUs that jointly optimizes placement, parallel execution, adaptive circuit cutting, and link-specific coordination for superconducting, trapped-ion, and neutral-atom systems.
DQR enables efficient scheduling and failover for cut quantum circuit fragments across local QPUs and remote simulators on real HPC hardware with low coordination overhead.
QuMod is a multi-programmable scheduler for modular QPUs that jointly optimizes qubit mapping, parallel circuit execution, measurement synchronization, and inter-QPU teleportation via dynamic circuits.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
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Post-Cut Metadata Inference Attacks on Quantum Circuit Cutting Pipelines
Post-cut metadata from quantum circuit fragments enables high-accuracy inference of algorithm family, cut mechanism, and Hamiltonian structure via machine learning on fragment width, depth, and gate counts.