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Yuntai Song

Publications and source records attributed to Yuntai Song.

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Approximate Quantum Error Correction at Chiral Topological Edges

Topologically ordered phases naturally realize quantum error correction through nonlocal encoding of quantum information. More recently, conformal field theories have been shown to realize approximate quantum error-correcting codes, but such constructions generally require fine tuning to criticality. Here we introduce a family of approximate quantum error-correcting codes realized by the chiral edges of two-dimensional topologically ordered phases. The proposed encoding combines the robustness of a gapped topological bulk with the flexibility of gapless edge conformal field theories. To characterize its robustness, we study coherent-information loss under local erasure. We derive an exact expression relating coherent-information loss to relative entropy, reducing the recoverability problem to universal properties of the edge theory. This leads to power-law scaling of coherent-information loss with the size of the erased region. We further show that, for geometrically local erasures near one edge, the two-dimensional chiral edge code is at least as robust as the dimensionally reduced CFT code, and is strictly more robust in several representative examples. For Abelian code subspaces, we further construct a power-law-range recovery map supported on the erased region together with a power-law-range buffer; this recovery map depends only on the code subspace, not on the unknown encoded state. We provide numerical calculations for lattice realizations of compact free boson and Ising CFT examples that support the theoretical predictions of the power-law exponents.

quant-ph

Neural Quantum States in Variational Monte Carlo Method: A Brief Summary

In this note, variational Monte Carlo method based on neural quantum states for spin systems is reviewed. Using a neural network as the wave function allows for a more generalized expression of various types of interactions, including highly non-local interactions, which are closely related to its non-linear activation functions. Additionally, neural networks can represent relatively complex wave functions with relatively small computational resources when dealing with higher-dimensional systems, which is undoubtedly a "flattening" advantage. In quantum-state tomography, the representation method of neural quantum states has already achieved significant results, hinting at its potential in handling larger-sized systems.

cond-mat.str-el