Experimental observation of average SPT phase in disordered Rydberg atom array at half-filling, supported by atom-atom correlations and slower edge spin decay in quench dynamics.
Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
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abstract
Tensor product state (TPS) based methods are powerful tools to efficiently simulate quantum many-body systems in and out of equilibrium. In particular, the one-dimensional matrix-product (MPS) formalism is by now an established tool in condensed matter theory and quantum chemistry. In these lecture notes, we combine a compact review of basic TPS concepts with the introduction of a versatile tensor library for Python (TeNPy) [https://github.com/tenpy/tenpy]. As concrete examples, we consider the MPS based time-evolving block decimation and the density matrix renormalization group algorithm. Moreover, we provide a practical guide on how to implement abelian symmetries (e.g., a particle number conservation) to accelerate tensor operations.
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UNVERDICTED 4representative citing papers
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.
A quantum channel applied near the entanglement cut maps the reduced density matrix of trivial gSPT states to non-trivial ones, thereby predicting their boundary CFT entanglement spectra.
A new fusion of adiabatic preconditioning and the Rodeo Algorithm, built hierarchically from solvable subsystems, enables robust exponential convergence for eigenstate preparation in the spin-1/2 XX model at high precision.
citing papers explorer
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Observation of average topological phase in disordered Rydberg atom array
Experimental observation of average SPT phase in disordered Rydberg atom array at half-filling, supported by atom-atom correlations and slower edge spin decay in quench dynamics.
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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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Hierarchical Fusion Method for Scalable Quantum Eigenstate Preparation
A new fusion of adiabatic preconditioning and the Rodeo Algorithm, built hierarchically from solvable subsystems, enables robust exponential convergence for eigenstate preparation in the spin-1/2 XX model at high precision.