An analytical Hessian-vector product kernel for arbitrary linear map compositions in tensor networks is derived via recursive tangent-state propagation, enabling scalable Riemannian trust-region optimization with major fidelity gains on spin-chain circuits.
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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.
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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Hessian-vector products for tensor networks via recursive tangent-state propagation
An analytical Hessian-vector product kernel for arbitrary linear map compositions in tensor networks is derived via recursive tangent-state propagation, enabling scalable Riemannian trust-region optimization with major fidelity gains on spin-chain circuits.
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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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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.