Random quantum circuits anti-concentrate in log depth

TL;DR

This paper demonstrates that random quantum circuits require at least logarithmic depth in the number of qubits to anti-concentrate, with precise bounds established for different circuit configurations, using a novel statistical mechanical approach.

Contribution

It establishes the minimum number of gates needed for anti-concentration in random quantum circuits and provides exact constants for different configurations, employing a new mapping to statistical mechanics.

Findings

At least rac{n  log(n)}{} gates are needed for anti-concentration.

O(n rac{n rac{1}{2}}{} gates are sufficient for anti-concentration.

Exact constants for the number of gates needed are computed for different circuit layouts.

Abstract

We consider quantum circuits consisting of randomly chosen two-local gates and study the number of gates needed for the distribution over measurement outcomes for typical circuit instances to be anti-concentrated, roughly meaning that the probability mass is not too concentrated on a small number of measurement outcomes. Understanding the conditions for anti-concentration is important for determining which quantum circuits are difficult to simulate classically, as anti-concentration has been in some cases an ingredient of mathematical arguments that simulation is hard and in other cases a necessary condition for easy simulation. Our definition of anti-concentration is that the expected collision probability, that is, the probability that two independently drawn outcomes will agree, is only a constant factor larger than if the distribution were uniform. We show that when the 2-local gates are each drawn from the Haar measure (or any two-design), at least $\Omega(n \log(n))$ gates (and thus $\Omega(\log(n))$ circuit depth) are needed for this condition to be met on an $n$ qudit circuit. In both the case where the gates are nearest-neighbor on a 1D ring and the case where gates are long-range, we show $O(n \log(n))$ gates are also sufficient, and we precisely compute the optimal constant prefactor for the $n \log(n)$. The technique we employ relies upon a mapping from the expected collision probability to the partition function of an Ising-like classical statistical mechanical model, which we manage to bound using stochastic and combinatorial techniques.