Random Quantum Circuits Transform Local Noise into Global White Noise

We study the distribution over measurement outcomes of noisy random quantum circuits in the regime of low fidelity, which corresponds to the setting where the computation experiences at least one gate-level error with probability close to one. We model noise by adding a pair of weak, unital, single-qubit noise channels after each two-qubit gate, and we show that for typical random circuit instances, correlations between the noisy output distribution p noisy and the corresponding noiseless output distribution p ideal shrink exponentially with the expected number of gate-level errors. Specifically, the linear cross-entropy benchmark F that measures this correlation behaves as F = exp ( - 2 s ϵ ± O ( s ϵ 2 ) ) , where ϵ is the probability of error per circuit location and s is the number of two-qubit gates. Furthermore, if the noise is incoherent—for example, depolarizing or dephasing noise—the total variation distance between the noisy output distribution p noisy and the uniform distribution p unif decays at precisely the same rate. Consequently, the noisy output distribution can be approximated as p noisy ≈ F p ideal + ( 1 - F ) p unif . In other words, although at least one local error occurs with probability 1 - F , the errors are scrambled by the random quantum circuit and can be treated as global white noise, contributing completely uniform output. Importantly, we upper bound the average total variation error in this approximation by O ( F ϵ s ) . Thus, the “white-noise approximation” is meaningful when ϵ s ≪ 1 , a quadratically weaker condition than the ϵ s ≪ 1 requirement to maintain high fidelity. The bound applies if the circuit size satisfies s ≥ Ω ( n log ( n ) ) , which corresponds to only logarithmic depth circuits, and if, additionally, the inverse error rate satisfies ϵ - 1 ≥ Ω ~ ( n ) , which is needed to ensure errors are scrambled faster than F decays. The white-noise approximation is useful for salvaging the signal from a noisy quantum computation; for example, it was an underlying assumption in complexity-theoretic arguments that noisy random quantum circuits cannot be efficiently sampled classically, even when the fidelity is low. 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.