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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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.