Particle-preserving fermionic shadows with mode-independent sample complexity (opens in new tab)

We consider the problem of learning expectation values of particle-preserving operators with respect to an unknown $\eta$-particle $n$-mode fermionic state via classical shadows. Our main application is to estimating overlaps with arbitrary Slater determinant states: While it is known that such overlaps can, in the average case, be learnt to a fixed additive precision with a constant number of samples, the best-known worst case bound is $\mathca...