A one-ancilla framework for QSAMPLE preparation via GQSP-based selective phase compilation embedded in fixed-point amplitude amplification, improving overlap dependence to inverse square-root minimum overlap.
Fixed-point quantum search with an optimal number of queries
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A recursive construction preserves O(sqrt(N)) quantum search complexity with local operations on tensor-decomposable partitions, eliminating the need for global diffusion via degeneracy in reflection angles.
A general framework and query-efficient algorithms for learning structured quantum unitaries based on Pauli spectrum support on small subgroups or sparsity, unifying prior results for multiple circuit classes.
Quantum rejection sampling applied to truncated Klein proposals yields quadratic speedup in dual-attack lattice Gaussian sampling, cutting Kyber attack costs by 9, 4, and 13 bits.
citing papers explorer
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Ancilla-Efficient QSAMPLE Preparation for Reversible Markov Chains
A one-ancilla framework for QSAMPLE preparation via GQSP-based selective phase compilation embedded in fixed-point amplitude amplification, improving overlap dependence to inverse square-root minimum overlap.
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Quantum Search without Global Diffusion
A recursive construction preserves O(sqrt(N)) quantum search complexity with local operations on tensor-decomposable partitions, eliminating the need for global diffusion via degeneracy in reflection angles.
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Efficient Learning of Structured Quantum Circuits via Pauli Dimensionality and Sparsity
A general framework and query-efficient algorithms for learning structured quantum unitaries based on Pauli spectrum support on small subgroups or sparsity, unifying prior results for multiple circuit classes.
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Improved Dual Attack and Trapdoor Sampling via Quantum Rejection Sampling
Quantum rejection sampling applied to truncated Klein proposals yields quadratic speedup in dual-attack lattice Gaussian sampling, cutting Kyber attack costs by 9, 4, and 13 bits.