QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
Lightsabre: A lightweight and enhanced sabre algorithm
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
years
2026 7verdicts
UNVERDICTED 7representative citing papers
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
Digital Annealer-assisted transpilation reduces CNOT counts by 13.7% on average (up to 57.4%) versus Qiskit on structured circuits, with a full-DA variant outperforming ISAAQ by 23.1%.
Position graph abstraction with memoized SABRE heuristics scales qubit mapping and routing for TI-QCCD architectures by caching repeated evaluations without altering decisions.
Noise-aware selection of circuit cutting strategies reduces execution overhead by 5-54x for 20-qubit circuits and makes 50-qubit circuit cutting feasible on non-uniformly noisy hardware.
SimpleTES scales test-time evaluation in LLMs to discover state-of-the-art solutions on 21 scientific problems across six domains, outperforming frontier models and optimization pipelines with examples like 2x faster LASSO and new Erdos constructions.
AlphaCNOT combines reinforcement learning with Monte Carlo Tree Search planning to reduce CNOT gate counts by up to 32% versus heuristics in quantum circuit synthesis.
citing papers explorer
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QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning
QAP-Router models qubit routing as dynamic QAP and applies RL with a solution-aware Transformer to cut CNOT counts by 12-30% versus industry compilers on real circuit benchmarks.
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CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
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Digital Annealer-Assisted Accuracy-First Quantum Circuit Transpilation with Integrated QUBO Mapping and Routing
Digital Annealer-assisted transpilation reduces CNOT counts by 13.7% on average (up to 57.4%) versus Qiskit on structured circuits, with a full-DA variant outperforming ISAAQ by 23.1%.
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Scaling Qubit Mapping and Routing With Position Graph Abstraction and Memoization
Position graph abstraction with memoized SABRE heuristics scales qubit mapping and routing for TI-QCCD architectures by caching repeated evaluations without altering decisions.
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Noise-aware selection of circuit cutting strategies under hardware noise non-uniformity
Noise-aware selection of circuit cutting strategies reduces execution overhead by 5-54x for 20-qubit circuits and makes 50-qubit circuit cutting feasible on non-uniformly noisy hardware.
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Evaluation-driven Scaling for Scientific Discovery
SimpleTES scales test-time evaluation in LLMs to discover state-of-the-art solutions on 21 scientific problems across six domains, outperforming frontier models and optimization pipelines with examples like 2x faster LASSO and new Erdos constructions.
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AlphaCNOT: Learning CNOT Minimization with Model-Based Planning
AlphaCNOT combines reinforcement learning with Monte Carlo Tree Search planning to reduce CNOT gate counts by up to 32% versus heuristics in quantum circuit synthesis.