For random MPS and Clifford+T circuits, increases in entanglement or T-count correlate with sharper loss minima and worse reconstruction under constrained neural capacity.
A singlet-gate makes distribution learning hard
2 Pith papers cite this work. Polarity classification is still indexing.
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Random states from symplectic and orthogonal unitaries show exponentially large strong state complexity and near-orthogonality, with average-case hardness for learning circuits from these groups.
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Comparing Classical Simulation and Sample-Based Learning of Quantum Systems
For random MPS and Clifford+T circuits, increases in entanglement or T-count correlate with sharper loss minima and worse reconstruction under constrained neural capacity.
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On the Complexity of Quantum States and Circuits from the Orthogonal and Symplectic Groups
Random states from symplectic and orthogonal unitaries show exponentially large strong state complexity and near-orthogonality, with average-case hardness for learning circuits from these groups.