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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DC-PINNs embed derivative constraints into PINN optimization using a minimum principle and adaptive balancing, reducing violations and improving fidelity on heat, finance, and fluid benchmarks.
A protocol extracts scaling dimensions of d=3 CFTs from the spectrum of qubit Hamiltonians on polyhedral lattices, achieving few-percent accuracy on the 3D Ising model with 20 qubits.
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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Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
DC-PINNs embed derivative constraints into PINN optimization using a minimum principle and adaptive balancing, reducing violations and improving fidelity on heat, finance, and fluid benchmarks.
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Qubit discretizations of d=3 conformal field theories
A protocol extracts scaling dimensions of d=3 CFTs from the spectrum of qubit Hamiltonians on polyhedral lattices, achieving few-percent accuracy on the 3D Ising model with 20 qubits.