On the role of the slowest observable in one-dimensional Markov processes to construct quasi-exactly-solvable generators with $N=2$ explicit levels

TL;DR

This paper explores how the slowest observable in one-dimensional Markov processes can be used to construct quasi-exactly-solvable quantum Hamiltonians with two explicit eigenstates, offering a more intuitive approach.

Contribution

It introduces a Markov process perspective focusing on the slowest observable to simplify the construction of quasi-exactly-solvable models with two explicit levels.

Findings

Reinterprets quasi-exactly-solvable models through Markov processes.

Shows the slowest observable $L_1(x)$ as a central object for model construction.

Applies the approach to Fokker-Planck and Markov jump generators.

Abstract

The construction of Quasi-Exactly-Solvable quantum Hamiltonians where only the two first eigenstates $\Phi_0(x)$ and $\Phi_1(x)$ of energies $E_0$ and $E_1$ are explicit is revisited from the point of view of one-dimensional Markov processes satisfying detailed-balance, whose generators are related to quantum Hamiltonians via similarity transformations. Here the lowest energy vanishes $E_0=0$ and is associated the conservation of probability and to the steady state $P_*(x)$, while $E_1>0$ is the rate that governs the exponential relaxation towards the steady-state, and is associated to the slowest observable $L_1(x)$ that corresponds to the ratio $ \frac{\Phi_1(x) }{\Phi_0(x)}$ of the two quantum eigenstates. Our main conclusion is that the Markov perspective leads to interesting re-interpretations and that the construction of quasi-exactly-solvable models with $N=2$ explicit levels is more intuitive and technically simpler if one takes the slowest observable $L_1(x)$ as the central object from which all the other properties can be reconstructed. This general approach is then applied to Fokker-Planck generators in continuous space and to Markov jump generators on the lattice.