QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
In: Proceedings of the 17th ACM International Conference on Computing Frontiers
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2representative citing papers
A time-aware beam search partitions quantum circuits across QPUs with quadratic scaling in qubits and lower communication overhead than static baselines.
citing papers explorer
-
Learning-Optimized Qubit Mapping and Reuse to Minimize Inter-Core Communication in Modular Quantum Architectures
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
-
Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing
A time-aware beam search partitions quantum circuits across QPUs with quadratic scaling in qubits and lower communication overhead than static baselines.