AQUIRE is the first error-aware adaptive Bayesian protocol for simultaneously estimating the mean and error of observables on qudit quantum computers using generalized Pauli operators and overlap grouping.
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6 Pith papers cite this work. Polarity classification is still indexing.
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A GNN predicts Gaussians over QAOA parameters to create graph-conditioned trust regions that reduce circuit evaluations for MaxCut from 85-343 down to 45 while keeping approximation ratios within 3 points of heuristics.
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
Demonstrates a quantum wire encoding using Rydberg atom chains to solve MWIS and QUBO problems on neutral atom arrays with reduced ancilla overhead and experimental validation.
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
Classical kernelisation fully reduces many small and sparse unit-disk graphs for MIS and MWIS native to Rydberg arrays, but dense graphs retain finite irreducible kernels, with vertex weights increasing reducibility and extended interaction ranges suppressing it.
citing papers explorer
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An Error-aware and Adaptive Method for the Estimation of Quantum Observables on Qudit-Based Quantum Computers
AQUIRE is the first error-aware adaptive Bayesian protocol for simultaneously estimating the mean and error of observables on qudit quantum computers using generalized Pauli operators and overlap grouping.
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Query-Efficient Quantum Approximate Optimization via Graph-Conditioned Trust Regions
A GNN predicts Gaussians over QAOA parameters to create graph-conditioned trust regions that reduce circuit evaluations for MaxCut from 85-343 down to 45 while keeping approximation ratios within 3 points of heuristics.
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Soft-Quantum Algorithms
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
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A quantum wire approach to weighted combinatorial graph optimisation problems
Demonstrates a quantum wire encoding using Rydberg atom chains to solve MWIS and QUBO problems on neutral atom arrays with reduced ancilla overhead and experimental validation.
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Variational decision diagrams for quantum-inspired machine learning applications
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
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Reducibility of native weighted graphs on Rydberg Arrays
Classical kernelisation fully reduces many small and sparse unit-disk graphs for MIS and MWIS native to Rydberg arrays, but dense graphs retain finite irreducible kernels, with vertex weights increasing reducibility and extended interaction ranges suppressing it.