Noise-induced shallow circuits and absence of barren plateaus

TL;DR

This paper investigates how uncorrected noise affects quantum circuits, showing that noise effectively makes circuits shallow, prevents barren plateaus, and enables classical algorithms to efficiently estimate observable expectation values.

Contribution

It proves noise truncates quantum circuits to logarithmic depth, prevents barren plateaus under non-unital noise, and introduces an efficient classical estimation algorithm for noisy circuits.

Findings

Noise truncates circuits to logarithmic depth.

No barren plateaus occur under non-unital noise.

Classical algorithms efficiently estimate observables in noisy circuits.

Abstract

Motivated by realistic hardware considerations of the pre-fault-tolerant era, we comprehensively study the impact of uncorrected noise on quantum circuits. We first show that any noise `truncates' most quantum circuits to effectively logarithmic depth, in the task of estimating observable expectation values. We then prove that quantum circuits under any non-unital noise exhibit lack of barren plateaus for cost functions composed of local observables. But, by leveraging the effective shallowness, we also design an efficient classical algorithm to estimate observable expectation values within any constant additive accuracy, with high probability over the choice of the circuit, in any circuit architecture. The runtime of the algorithm is independent of circuit depth, and for any inverse-polynomial target accuracy, it operates in polynomial time in the number of qubits for one-dimensional architectures and quasi-polynomial time for higher-dimensional ones. Taken together, our results showcase that, unless we carefully engineer the circuits to take advantage of the noise, it is unlikely that noisy quantum circuits are preferable over shallow quantum circuits for algorithms that output observable expectation value estimates, like many variational quantum machine learning proposals. Moreover, we anticipate that our work could provide valuable insights into the fundamental open question about the complexity of sampling from (possibly non-unital) noisy random circuits.