Quantum algorithms achieve polynomial advantage for synchronization estimation and super-polynomial advantage for no-phase-locking certification in higher-order simplicial Kuramoto models under stated assumptions.
Kerenidis \ and\ author A
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The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
Framework using Butterfly circuits, layer-wise training and parallel parameter-shift reduces QNN training cost to O(log n) circuit evaluations, validated on MIMIC-III clinical data with hardware execution at 16 qubits and simulation at 32.
citing papers explorer
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Efficient Quantum Algorithms for Higher-Order Coupled Oscillators
Quantum algorithms achieve polynomial advantage for synchronization estimation and super-polynomial advantage for no-phase-locking certification in higher-order simplicial Kuramoto models under stated assumptions.
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Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
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Scalable On-Hardware Training of Quantum Neural Networks and Application to Clinical Data Imputation
Framework using Butterfly circuits, layer-wise training and parallel parameter-shift reduces QNN training cost to O(log n) circuit evaluations, validated on MIMIC-III clinical data with hardware execution at 16 qubits and simulation at 32.