Empirical study finds neural-network learning difficulty (via Hessian eigenvalue and random subspace optimization) correlates with classical simulation hardness parameterized by MPS bond dimension and T-gate count.
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Comparing Classical Simulation and Sample-Based Learning of Quantum Systems: Learning the Hardness of Quantum Systems from Samples
Empirical study finds neural-network learning difficulty (via Hessian eigenvalue and random subspace optimization) correlates with classical simulation hardness parameterized by MPS bond dimension and T-gate count.