A 10-qubit convolutional quantum graph neural network fed by autoencoder-compressed jet data achieves performance comparable to classical graph networks in distinguishing boosted Z jets from gluon jets.
Verdon , author T
11 Pith papers cite this work, alongside 60 external citations. Polarity classification is still indexing.
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Quantum-inspired observables reveal poor signal routing in standard spectral GNNs and motivate Schrödinger GNNs with superior propagation capacity.
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.
Proposes a hybrid quantum-classical framework for running event-based graph neural networks on neutral-atom processors by mapping events to atoms and programming the Rydberg Hamiltonian to realize message passing.
P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
Equivariant sp-QCNN encodes general symmetries with group theory, splits circuits at pooling layers to preserve symmetry while enabling parallel measurements, and shows improved efficiency and trainability over standard equivariant QCNNs in noisy quantum data classification.
Learning-induced spectral structure in hybrid quantum models is diagnosed by edge-resolved two-boson interference correlated with Fiedler cuts and by absolute Bloch drift that separates anomalies from benign states.
COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.
A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.
For noisy near-term quantum devices, the paper recommends shallow angle encoding over amplitude encoding once two-qubit error rates exceed roughly 10^-3.
citing papers explorer
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Quantum enhanced identification of boosted jets with quantum graph neural networks
A 10-qubit convolutional quantum graph neural network fed by autoencoder-compressed jet data achieves performance comparable to classical graph networks in distinguishing boosted Z jets from gluon jets.
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Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
Quantum-inspired observables reveal poor signal routing in standard spectral GNNs and motivate Schrödinger GNNs with superior propagation capacity.
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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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Analog Quantum Asynchronous Event-Based Graph Neural Network
Proposes a hybrid quantum-classical framework for running event-based graph neural networks on neutral-atom processors by mapping events to atoms and programming the Rydberg Hamiltonian to realize message passing.
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Physics Guided Generative Optimization for Trotter Suzuki Decomposition
P-GONE applies generative ML to optimize Trotter-Suzuki decompositions, reporting up to 19.4x circuit depth reduction at F >= 0.95 versus Qiskit baselines on structured Hamiltonians.
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Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
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Resource-efficient equivariant quantum convolutional neural networks
Equivariant sp-QCNN encodes general symmetries with group theory, splits circuits at pooling layers to preserve symmetry while enabling parallel measurements, and shows improved efficiency and trainability over standard equivariant QCNNs in noisy quantum data classification.
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Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning
Learning-induced spectral structure in hybrid quantum models is diagnosed by edge-resolved two-boson interference correlated with Fiedler cuts and by absolute Bloch drift that separates anomalies from benign states.
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Conservative quantum offline model-based optimization
COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.
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Quantum Observers: A NISQ Hardware Demonstration of Chaotic State Prediction Using Quantum Echo-state Networks
A quantum echo-state network is implemented on NISQ superconducting qubits and shown to predict long chaotic trajectories from the Lorenz system with memory persisting over 100 times the median T1/T2 time.
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Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines
For noisy near-term quantum devices, the paper recommends shallow angle encoding over amplitude encoding once two-qubit error rates exceed roughly 10^-3.