Quantum Approximation of Normalized Schatten Norms and Applications to Learning

Efficient measures to determine similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be $\mathit{\text{efficiently}}\mathit{\text{estimated}}$. Given two quantum operations, $U_1$ and $U_2$, represented in their circuit forms, we first develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference ($\mathrm{\parallel }{U}_1-U_2{\mathrm{\parallel }}_{S_2}$) with precision $\epsilon$, using only one clean qubit and one classical random variable. We prove a Poly$(\frac{1}{\epsilon })$ upper bound on the sample complexity, which is independent of the size of the quantum system. We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states ($F$): If $\mathrm{\parallel }{U}_1-U_2{\mathrm{\parallel }}_{S_2}$ is sufficiently small (e.g. $\le \frac{\epsilon }{1+\sqrt{2(1\mathrm{/}\delta -1)}}$) then the fidelity of states obtained by processing the same randomly and uniformly picked pure state, $\mathrm{\mid }\psi ⟩$, is as high as needed ($F(U_1\mathrm{\mid }\psi ⟩,U_2\mathrm{\mid }\psi ⟩)\ge 1-\epsilon$) with probability exceeding $1-\delta$. We provide example applications of this efficient similarity metric estimation framework to quantum circuit learning tasks, such as finding the square root of a given unitary operation.