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arXiv2026

Quantum information spreading via higher-order operator correlators

We provide a perspective into measuring the quantum information spreading by introducing N-operator correlators. Leveraging the idea from N-point correlation function from cosmology, we formulate a generalized Out-of-Time-Ordered Correlator (OTOC), called N-Operator Correlator (NOC). Such formulation can reduce the effect of restricted analysis associated with selecting only two operators for OTOC. NOC can take into account a larger set of measurement probes in measuring the degree of quantum information spreading. We demonstrate these concepts with two analytically tractable toy systems with computing thermal averages of operator time evolutions at a finite temperature: a single qubit in longitudinal field and XYZ two-qubit system. Our perspective serves as a basic introduction to generalized operator correlators in quantum mechanics.

quant-ph↗

arXiv2026

Gibbs randomness-compression proposition

A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a lossy compression process. In building this connection, we use a performance of a learning task as a probe of compression in iterative compress-train cycles. This can be thought as iterative coarse-graining from statistical mechanics perspective using thermodynamic efficiency as a probe. We formulate this connection via comonotonic relationship within a very small decrease in compression ratio and the performance. We have showcase the validity of this proposition with a canonical vision task in deep learning with three different model compression processes as {\it a baseline model}. We use the following, simpler to more complex model compression approaches: (1) random pruning,(2) magnitude pruning, and (3) a more complex compression by using dual tomographic compression, which utilizes compressed sensing in dual fashion which is introduced as a new method. We use remaining weights of deep learning network as a measurement vector where we measure the Gibbs entropy. We show case the idea that there is an inherent computable connection between compression probed by performance and randomness from an entropy measure on the learned model.

stat.ML↗