A distributed quantum Gaussian process framework is introduced for multi-agent systems with a consensus Riemannian ADMM algorithm, evaluated on elevation datasets via quantum simulator.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Combining dynamical decoupling and zero-noise extrapolation on real quantum hardware improves energy gap estimates by at least 60% and reduces time-evolution errors by up to 99% for the Ising model in dynamic circuit Hamiltonian simulations.
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.
citing papers explorer
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Distributed Quantum Gaussian Processes for Multi-Agent Systems
A distributed quantum Gaussian process framework is introduced for multi-agent systems with a consensus Riemannian ADMM algorithm, evaluated on elevation datasets via quantum simulator.
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Error Mitigation in Dynamic Circuits for Hamiltonian Simulation
Combining dynamical decoupling and zero-noise extrapolation on real quantum hardware improves energy gap estimates by at least 60% and reduces time-evolution errors by up to 99% for the Ising model in dynamic circuit Hamiltonian simulations.
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A Comprehensive Analysis of Accuracy and Robustness in Quantum Neural Networks
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.