Random quantum circuits anti-concentrate in log depth
| Autor: | Alexander M. Dalzell, Nicholas Hunter-Jones, Fernando G. S. L. Brandão |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer Science::Hardware Architecture
Quantum Physics Condensed Matter - Strongly Correlated Electrons Computer Science::Emerging Technologies Statistical Mechanics (cond-mat.stat-mech) Strongly Correlated Electrons (cond-mat.str-el) General Engineering FOS: Physical sciences General Earth and Planetary Sciences Quantum Physics (quant-ph) Condensed Matter - Statistical Mechanics General Environmental Science |
| Popis: | 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. Comment: 46 pages, 7 figures. v2: Reformatted, fixed typos, added figure 3 |
| Databáze: | OpenAIRE |
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