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Aug 1, 2023 at 18:01 comment added Quantum Mechanic @gIS well... that's a better answer than mine!
Aug 1, 2023 at 17:51 comment added glS You can also strengthen the inequality you cited replacing $d$ with the rank of $A$, which you can prove observing your argument also holds if instead of $I$ you use the projection on the support of $A$ (at least it works for positive semidefinite operators; the argument might need some adjustment for the more general case, I'm not sure). These inequalities are a standard tool discussing t-designs, see eg en.wikipedia.org/wiki/Welch_bounds
Aug 1, 2023 at 17:46 comment added glS I think you can say something quite a bit stronger: you always have the opposite inequality, $\|A\|_1\ge \|A\|_2$. You see it easily from the fact that $\|A\|_1$ is the sum of the singular values of $A$, while $\|A\|_2^2$ is the sum of the squares of the singular values. Thus $\|A\|_1^2 \ge \|A\|_2^2$, as the square of a sum is always larger than the sum of squares, which is equivalent to $\|A\|_1 \ge \|A\|_2$.
Aug 1, 2023 at 14:17 history answered Quantum Mechanic CC BY-SA 4.0