Matrix concentration inequalities and efficiency of random universal sets of quantum gates

TL;DR

This paper derives bounds on the probability that a random set of quantum gates forms an approximate t-design, providing insights into the number of gates needed and concentration properties, with implications for quantum information processing.

Contribution

The paper introduces new probabilistic bounds for approximate t-designs in quantum gates and analyzes their concentration, extending understanding of random quantum gate sets.

Findings

Probability bounds for approximate t-design formation

Number of gates needed for desired approximation and confidence

Concentration behavior of the approximation parameter

Abstract

For a random set $\mathcal{S} \subset U(d)$ of quantum gates we provide bounds on the probability that $\mathcal{S}$ forms a $\delta$-approximate $t$-design. In particular we have found that for $\mathcal{S}$ drawn from an exact $t$-design the probability that it forms a $\delta$-approximate $t$-design satisfies the inequality $\mathbb{P}\left(\delta \geq x \right)\leq 2D_t \, \frac{e^{-|\mathcal{S}| x \, \mathrm{arctanh}(x)}}{(1-x^2)^{|\mathcal{S}|/2}} = O\left( 2D_t \left( \frac{e^{-x^2}}{\sqrt{1-x^2}} \right)^{|\mathcal{S}|} \right)$, where $D_t$ is a sum over dimensions of unique irreducible representations appearing in the decomposition of $U \mapsto U^{\otimes t}\otimes \bar{U}^{\otimes t}$. We use our results to show that to obtain a $\delta$-approximate $t$-design with probability $P$ one needs $O( \delta^{-2}(t\log(d)-\log(1-P)))$ many random gates. We also analyze how $\delta$ concentrates around its expected value $\mathbb{E}\delta$ for random $\mathcal{S}$. Our results are valid for both symmetric and non-symmetric sets of gates.