For real square matrices $A_0$ and $A_{1}$ with $A^{T}_0+A_0+A^{T}_{1}+A_1\leq{-\gamma{I}}$, where $\leq$ denotes the negative semi-definiteness of the left argument with respect to the right, $\gamma>0$ and $I$ is the identity matrix of suitable dimension.
Is it possible to show that $x^T\Big(e^{A^{T}_{1}}*e^{A^{T}_{0}}*e^{A_{0}}*e^{A_{1}}\Big)x\leq\|x\|^2$ for a non-zero vector $x$? I know if $A_0$ and $A_1$ are normal matrices and $A_0, A_1$ are commutative to each other, the mentioned claim holds immediately. But for the case when these matrices are not normal and do not commute, does the claim hold?
My approach: $x^T\Big(e^{A^{T}_{1}}*e^{A^{T}_{0}}*e^{A_{0}}*e^{A_{1}}\Big)x\leq\|x\|^2\|e^{A_0}*e^{A_1}\|^2\leq\|x\|^2\|e^{A_0}*e^{A_1}\|_F^2=\|x\|^2~\|e^{A_0}\|_F^2\|e^{A_1}\|_F^2=\|x\|^2\text{Tr}\left(e^{A^T_0}e^{A_0}\right)\text{Tr}\left(e^{A^T_1}e^{A_1}\right)\leq\|x\|^2\text{Tr}\left(e^{A^T_0+A_0}\right)\text{Tr}\left(e^{A^T_1+A_1}\right)$ by virtue of equation (1.3) of https://epubs.siam.org/doi/pdf/10.1137/0609012.
But I do not see a way forward. Any hints or suggestions are greatly appreciated.