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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Lightsabre: A lightweight and enhanced sabre algorithm
11 Pith papers cite this work. Polarity classification is still indexing.
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dSABRE cuts geometric-mean EPR consumption by 41-44% versus TeleSABRE on 18 benchmark circuits through intra-core priority, a five-term teleportation scorer with capacity penalty, proactive congestion relief, and BFS-layer extended sets.
A co-design method for frequency allocation and noise-aware transpilation in tunable-coupler quantum systems yields 8.9% lower log-infidelity cost and 6.8% shorter circuits than SABRE on SNAIL architectures.
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.
Canopus unifies qubit mapping and routing across quantum ISAs by modeling synthesis costs via canonical two-qubit gate forms, achieving 15-35% lower routing overhead than prior methods on varied backends and topologies.
TeleSABRE extends SABRE to combine intra-core SWAPs with inter-core teleportation, reporting a 28% reduction in inter-core operations on benchmarks for multi-core quantum architectures.
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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.