A zero-mode gauge fixing technique truncates bonds in loopy tensor networks by exploiting linear dependencies in the metric tensor of cut-bond states, applied to iPEPS representations of the finite-temperature 2D Z2 lattice gauge theory.
Vidal ,\ https://link.aps.org/doi/10.1103/PhysRevLett.101.110501 journal journal Phys
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Covariant holographic entanglement entropy determines bulk radial geometry in stationary homogeneous 3D spacetimes when reconstructions from fixed-kappa families agree on a common radial coordinate.
Light cone cancellation decomposes VQE circuits for Max-Cut into smaller subcircuits, yielding higher approximation ratios on simulated noisy backends up to 100 qubits compared to standard VQE.
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.
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Truncating loopy tensor networks by zero-mode gauge fixing: the $Z_2$ lattice gauge theory at finite temperature
A zero-mode gauge fixing technique truncates bonds in loopy tensor networks by exploiting linear dependencies in the metric tensor of cut-bond states, applied to iPEPS representations of the finite-temperature 2D Z2 lattice gauge theory.
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Geometry-Induced Long-Range Correlations in Recurrent Neural Network Quantum States
Dilated RNN wave functions induce power-law correlations for the critical 1D transverse-field Ising model and the Cluster state, unlike the exponential decay of conventional RNN ansatze.
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Covariant Holographic Entanglement Entropy Inversion to Reconstruct Bulk Geometry
Covariant holographic entanglement entropy determines bulk radial geometry in stationary homogeneous 3D spacetimes when reconstructions from fixed-kappa families agree on a common radial coordinate.
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Light Cone Cancellation for Variational Quantum Eigensolver in Solving Noisy Max-Cut
Light cone cancellation decomposes VQE circuits for Max-Cut into smaller subcircuits, yielding higher approximation ratios on simulated noisy backends up to 100 qubits compared to standard VQE.
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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.