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.
Adiabatic quantum computation
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The thesis introduces a topology-aware tensor-network heuristic called SpinGlassPEPS.jl and thermodynamic metrics to benchmark quantum annealers on Ising problems while accounting for dissipation and effective temperature.
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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.
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Neural and Tensor Networks in the Study of Quantum Annealing Processors
The thesis introduces a topology-aware tensor-network heuristic called SpinGlassPEPS.jl and thermodynamic metrics to benchmark quantum annealers on Ising problems while accounting for dissipation and effective temperature.