Consider a probability distribution $\pi$ on the real axis that has a density (w.r.t Lebesgue) proportional to $e^{-V(x)}$, where $V(\cdot)$ is a potential function. For any reasonable volatility function $\sigma:\mathbb{R} \to (0:+\infty)$ the diffusion $$ dX^{\sigma}_t = [ -\frac{1}{2} \sigma(X_t^{\sigma})^2 V'(X_t^{\sigma}) + \sigma(X_t^{\sigma}) \sigma'(X_t^{\sigma}) ] dt + \sigma(X_t^{\sigma}) \, dW_t $$ has $\pi$ as unique invariant distribution.

**Question:**
Given two volatility functions $\sigma_1, \sigma_2$, are there tractable ways of comparing the speed of convergence to equilibrium of the two associated diffusions?

For example, if $\sigma_2(x) = \alpha \cdot \sigma_1(x)$, the diffusion $X^{\sigma_2}$ is just $X^{\sigma_1}$ slowed down by a factor $\alpha$: any ways of comparing the two diffusions should say that if $\alpha > 1$ then $X^{\sigma_2}$ converges 'faster' than $X^{\sigma_1}$. Spectral Gaps work but are not very tractable when comparing two non-proportional diffusions. Is it hopeless ?

**Motivations:**
I consider several MCMC algorithms with target density $\pi$: each one of them, after some time-rescaling, looks like a diffusion $X^{\sigma}$. Which algorithm is the best $i.e.$ what diffusion $X^{\sigma}$ mixes the fastest ?