Sparse power-of-two circuit connectivity provides task-dependent advantages for variational quantum learning, and a Monna-map qubit reordering enables short-range circuits to solve long-range problems.
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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.
H-EFT-VA enforces a UV-cutoff initialization to guarantee inverse-polynomial gradient variance while preserving volume-law entanglement and near-Haar purity in variational quantum algorithms.
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.
citing papers explorer
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Variational Learning with Sparse Long-range Entangling Gates
Sparse power-of-two circuit connectivity provides task-dependent advantages for variational quantum learning, and a Monna-map qubit reordering enables short-range circuits to solve long-range problems.
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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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H-EFT-VA: An Effective-Field-Theory Variational Ansatz with Provable Barren Plateau Avoidance
H-EFT-VA enforces a UV-cutoff initialization to guarantee inverse-polynomial gradient variance while preserving volume-law entanglement and near-Haar purity in variational quantum algorithms.
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Quantum Convolutional Neural Networks are Effectively Classically Simulable
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.
- Distributions of Noisy Expectation Values over Sets of Measurement Operators