Nielsen quantum circuit complexity is positioned as a topological distance for unsupervised learning of topological order, with theorems linking it to Bures distance and entanglement to yield practical fidelity- and entanglement-based kernels demonstrated on XXZ chains and toric code.
Machine learning on quantum experimental data toward solving quantum many-body problems,
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Quantum circuit complexity and unsupervised machine learning of topological order
Nielsen quantum circuit complexity is positioned as a topological distance for unsupervised learning of topological order, with theorems linking it to Bures distance and entanglement to yield practical fidelity- and entanglement-based kernels demonstrated on XXZ chains and toric code.