Multivariate DQI uses N-variable polynomials for weighted Max-LINSAT, derives closed-form asymptotics for expectation and concentration, provides a single-decoder preparation circuit, and shows outperformance over weighted Prange for some OPI cases while extending to Hamiltonian DQI.
A variational eigenvalue solver on a photonic quantum processor.Nature communications, 5(1):4213
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Dynamarq is a new scalable benchmarking framework that defines structural features for dynamic quantum circuits and uses statistical models to predict hardware fidelity with transferable parameters.
Quantum algorithm approximates k-th spectral gap Δ_k and midpoint μ_k of Hermitian matrix to εΔ_k error with O(N²/(ε² Δ_k²) polylog) QRAM complexity, claiming speedup for large gaps, plus Ω(N²) black-box lower bound.
citing papers explorer
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Multivariate Decoded Quantum Interferometry for Weighted Optimization
Multivariate DQI uses N-variable polynomials for weighted Max-LINSAT, derives closed-form asymptotics for expectation and concentration, provides a single-decoder preparation circuit, and shows outperformance over weighted Prange for some OPI cases while extending to Hamiltonian DQI.
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Characterizing and Benchmarking Dynamic Quantum Circuits
Dynamarq is a new scalable benchmarking framework that defines structural features for dynamic quantum circuits and uses statistical models to predict hardware fidelity with transferable parameters.
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Spectral Gaps with Quantum Counting Queries and Oblivious State Preparation
Quantum algorithm approximates k-th spectral gap Δ_k and midpoint μ_k of Hermitian matrix to εΔ_k error with O(N²/(ε² Δ_k²) polylog) QRAM complexity, claiming speedup for large gaps, plus Ω(N²) black-box lower bound.