Compares shadow-based CNNs and physics-informed QCNNs for predicting low-energy subspace overlaps in quenched 10-qubit Heisenberg chains, reporting regime-dependent R^2 performance with QCNNs more stable overall.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Hybrid quantum-classical neural network experimentally classifies topological ground states of surface-code lattices up to 4x4 sites from product states, achieving >85% single-shot and >99% averaged accuracy even under single-qubit Pauli errors.
A hybrid neural network combining a shallow parameterized quantum circuit with a classical neural network reduces sample complexity for topological phase recognition by approximately one order of magnitude versus classical neural networks on randomized Pauli measurements.
citing papers explorer
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Learning Low-Energy Subspace Overlaps in Many-Body Systems with Measurement-Based and Coherent Quantum Strategies
Compares shadow-based CNNs and physics-informed QCNNs for predicting low-energy subspace overlaps in quenched 10-qubit Heisenberg chains, reporting regime-dependent R^2 performance with QCNNs more stable overall.
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Hybrid Quantum-Classical Neural Networks for Recognizing Quantum Phases
Hybrid quantum-classical neural network experimentally classifies topological ground states of surface-code lattices up to 4x4 sites from product states, achieving >85% single-shot and >99% averaged accuracy even under single-qubit Pauli errors.
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Hybrid quantum-classical neural network for sample-efficient recognition of topological phases
A hybrid neural network combining a shallow parameterized quantum circuit with a classical neural network reduces sample complexity for topological phase recognition by approximately one order of magnitude versus classical neural networks on randomized Pauli measurements.