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Caisheng Cheng

Publications and source records attributed to Caisheng Cheng.

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Typical Mixing and Rare-State Bottlenecks in Open Quantum Systems

Mixing in open quantum systems is often summarized by a single worst-case time, even though that benchmark can be set by exponentially rare initial states. We show that for broad unstructured ensembles the nonlinear trace-distance relaxation curve itself concentrates around a deterministic mean. For Haar-random pure states this yields fixed-time concentration of the instantaneous trace distance to the steady state, which we term vertical concentration since typical relaxation curves bundle along the distance axis. Whenever the mean curve crosses the distance threshold with a finite slope, it converts this vertical concentration into a horizontal concentration of the mixing time, extending typicality from standard physical observables to a fundamentally non-observable dynamical quantity. This sharp concentration naturally raises the question of how the typical mixing timescale compares to the worst-case benchmark. We show that in a one-mode tail regime, this separation is controlled by the logarithmic ratio of extremal to typical initial-state overlaps for the slow left eigenoperator. This rare-state bottleneck law yields a hierarchy that is logarithmic in skin-effect settings, linear for boundary-supported many-body slow modes, and exponential in a protected-sector family where generic states mix rapidly while rare states stagnate. The framework also extends beyond Haar to exact and approximate unitary 2-designs and Hilbert-Schmidt/induced ensembles.

quant-ph

Physically natural metric-measure Lindbladian ensembles and their learning hardness

In open quantum systems, a basic question at the interface of quantum information, statistical physics, and many-body dynamics is how well can one infer the structure of noise and dissipation generators from finite-time measurement statistics alone. Motivated by this question, we study the learnability and cryptographic applications of random open-system dynamics generated by Lindblad-Gorini-Kossakowski-Sudarshan (GKSL) master equations. Working in the affine hull of the GKSL cone, we introduce physically motivated ensembles of random local Lindbladians via a linear parametrisation around a reference generator. On top of this geometric structure, we extend statistical query (SQ) and quantum-process statistical query (QPStat) frameworks to the open-system setting and prove exponential (in the parameter dimension $M$) lower bounds on the number of queries required to learn random Lindbladian dynamics. In particular, we establish average-case SQ-hardness for learning output distributions in total variation distance and average-case QPStat-hardness for learning Lindbladian channels in diamond norm. To support these results physically, we derive a linear-response expression for the ensemble-averaged total variation distance and verify the required nonvanishing scaling in a random local amplitude-damping chain. Finally, we design two Lindbladian physically unclonable function (Lindbladian-PUF) protocols based on random Lindbladian ensembles with distribution-level and tomography-based verification, thereby providing open-system examples where learning hardness can be translated into cryptographic security guarantees.

quant-ph