Number-conserving fermionic shadow tomography estimates all k-body correlations in η-particle N-mode states using O_k(η^k/ε²) samples independent of N, with a matching Ω_k(η^k/ε²) lower bound for single-copy adaptive protocols.
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Coherent quantum inference achieves O(1/ε) sample complexity for d-dimensional quantum purity amplification, exponentially better than the Ω(d/ε) required by any incoherent measurement-mediated protocol.
Optimal algorithms achieve query complexities Θ(d/ε²) for incoherent access, Θ(d/ε) for coherent access, and Θ(√d/ε) for source-code access in quantum channel certification to unitary, exactly matching prior lower bounds.
CBNE enables estimation of nonlinear quantum properties such as higher-order expectations from a single randomized measurement setting under sufficient system dimension or ancillary qubits.
QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
A new sequential Bayesian reconstruction method based on NV-center quantum Hamiltonian learning reconstructs dominant structures in synthetic dynamic 2D magnetic fields with low RMSE but only partially identifies the shared coupling parameter.
Proposes a context-aware unit testing framework for quantum subroutines modeled as parametrized quantum channels, using probabilistic assertions and demonstrated on GHZ preparation and Shor's algorithm subroutines.
Hybrid quantum interior point methods for linear programming have no practical runtime advantage over classical solvers like HiGHS on realistic instances because their quantum lower bounds already exceed classical performance under optimistic assumptions.
Perspective review comparing variational and feedback quantum algorithms for simulating phase transitions in quantum many-body systems, highlighting barren plateaus and advocating physics-informed hybridization.
Lecture notes and accompanying library teach replica tensor network methods to compute circuit-averaged observables in random quantum circuits by mapping them to classical statistical mechanics models.
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