A note on statistical tensors in quantum mechanics

Lawrence C. Biedenharn

G. Sobotta

Xhek Turkeshi

We present a pedagogical, hands-on tutorial on \emph{replica tensor-network} techniques for random quantum circuits. At its core, the method recasts circuit-averaged observables acting on multiple copies of the system as the contraction of a classical tensor network, equivalently the partition function of a statistical-mechanics model whose effective spins live in the commutant of the gate ensemble. The framework is general: changing the observable or the initial state modifies only the replica boundary conditions, while changing the ensemble modifies the bulk tensors. Focusing on quantum-information diagnostics, from metrics of wavefunction spreadings to entanglement quantifiers, we illustrate the approach in both clean and noisy random unitary circuits. We then briefly explain how the methodology extends to other ensembles, such as orthogonal or Clifford circuits. The lecture notes are accompanied by \texttt{ReplicaTN}, a self-contained C++/Python library and pedagogical notebooks.

Loyal Durand

I show how the pressure in Fermi and Bose systems, identified in standard discussions of quantum statistical mechanics by the use of thermodynamic analogies, can be derived directly in terms of the flux of momentum across a surface by using the quantum mechanical stress tensor. In this approach, which is analogous to classical kinetic theory, the pressure is naturally defined locally. The approach leads to a simple interpretation of the pressure in terms of the momentum flow encoded in the wave functions. The stress-tensor and thermodynamic approaches are related by an interesting application of boundary perturbation theory for quantum systems. I investigate the properties of quasi-continuous systems, the relations for Fermi and Bose pressures, shape-dependent effects and anisotropies, and the treatment of particles in external fields, and note several interesting problems for graduate courses in statistical mechanics.

Walter Felipe Wreszinski, G. Scharf

浩 市村