Hamming Weight Operators and an adaptive QAOA variant confine evolution to feasible states by construction, delivering faster convergence and roughly half the gate count versus penalty methods on finance and physics tasks.
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Fourier-based LCU decomposes diagonal and non-diagonal unitaries into hardware-friendly forms for QAOA-style optimization, trading circuit depth for sampling overhead with performance guarantees.
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.
citing papers explorer
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Constraint-Aware Quantum Optimization via Hamming Weight Operators
Hamming Weight Operators and an adaptive QAOA variant confine evolution to feasible states by construction, delivering faster convergence and roughly half the gate count versus penalty methods on finance and physics tasks.
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Efficient Fourier-Based Linear Combination of Unitaries and Applications in Quantum Optimization
Fourier-based LCU decomposes diagonal and non-diagonal unitaries into hardware-friendly forms for QAOA-style optimization, trading circuit depth for sampling overhead with performance guarantees.
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Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.