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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3 Pith papers cite this work. Polarity classification is still indexing.
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Trapped-ion quantum fine-tuning of AI models shows linear energy scaling and 24% better classification error than classical logistic regression or SVM baselines, with a projected energy break-even at 34 qubits.
Presents a concrete quantum oracle for bilinear Diophantine equations enabling factoring of n-bit biprimes with 2n-5 qubits or fewer and near-100% simulated success for numbers up to 35 bits.
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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Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
Trapped-ion quantum fine-tuning of AI models shows linear energy scaling and 24% better classification error than classical logistic regression or SVM baselines, with a projected energy break-even at 34 qubits.
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Efficient Quantum Oracle for Solving Bilinear Diophantine Equations on Digital Quantum Computers
Presents a concrete quantum oracle for bilinear Diophantine equations enabling factoring of n-bit biprimes with 2n-5 qubits or fewer and near-100% simulated success for numbers up to 35 bits.