Random quantum circuits transform local noise into global white noise (2111.14907v1)

Abstract: We study the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime. We show that, for local noise that is sufficiently weak and unital, correlations (measured by the linear cross-entropy benchmark) between the output distribution $p_{\text{noisy}}$ of a generic noisy circuit instance and the output distribution $p_{\text{ideal}}$ of the corresponding noiseless instance shrink exponentially with the expected number of gate-level errors, as $F=\text{exp}(-2s\epsilon \pm O(s\epsilon^2))$, where $\epsilon$ is the probability of error per circuit location and $s$ is the number of two-qubit gates. Furthermore, if the noise is incoherent, the output distribution approaches the uniform distribution $p_{\text{unif}}$ at precisely the same rate and can be approximated as $p_{\text{noisy}} \approx Fp_{\text{ideal}} + (1-F)p_{\text{unif}}$, that is, local errors are scrambled by the random quantum circuit and contribute only white noise (uniform output). Importantly, we upper bound the total variation error (averaged over random circuit instance) in this approximation as $O(F\epsilon \sqrt{s})$, so the "white-noise approximation" is meaningful when $\epsilon \sqrt{s} \ll 1$, a quadratically weaker condition than the $\epsilon s\ll 1$ requirement to maintain high fidelity. The bound applies when the circuit size satisfies $s \geq \Omega(n\log(n))$ and the inverse error rate satisfies $\epsilon^{-1} \geq \tilde{\Omega}(n)$. The white-noise approximation is useful for salvaging the signal from a noisy quantum computation; it was an underlying assumption in complexity-theoretic arguments that low-fidelity random quantum circuits cannot be efficiently sampled classically. Our method is based on a map from second-moment quantities in random quantum circuits to expectation values of certain stochastic processes for which we compute upper and lower bounds.