A generative model learns patterns from adaptive QAOA circuits to generate high-quality shallow quantum circuits for Max-E3-SAT that scale better than variational baselines.
Multi-angle quantum approximate optimization algorithm
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
A sandbox platform enables end-to-end hybrid workflows that reduce graph problems, run QAOA on IBM hardware up to 128 qubits, and refine outputs classically for problems including vertex cover and clique.
citing papers explorer
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Q3SAT-GPT: A Generative Model for Discovering Quantum Circuits for the 3-SAT Problem
A generative model learns patterns from adaptive QAOA circuits to generate high-quality shallow quantum circuits for Max-E3-SAT that scale better than variational baselines.
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Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
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Experimental Workflows for Combinatorial Optimization: Towards Quantum Advantage
A sandbox platform enables end-to-end hybrid workflows that reduce graph problems, run QAOA on IBM hardware up to 128 qubits, and refine outputs classically for problems including vertex cover and clique.