Four Deviations Suffice for Rank 1 Matrices

We prove a matrix discrepancy bound that strengthens the famous Kadison-Singer result of Marcus, Spielman, and Srivastava. Consider any independent scalar random variables $ξ_1, \ldots, ξ_n$ with finite support, e.g. $\{ \pm 1 \}$ or $\{ 0,1 \}$-valued random variables, or some combination thereof. Let $u_1, \dots, u_n \in \mathbb{C}^m$ and $σ^2 = \left\| \sum_{i=1}^n \text{Var}[ ξ_i ] (u_i u_i^{*})^2 \right\|.$ Then there exists a choice of outcomes $\varepsilon_1,\ldots,\varepsilon_n$ in the support of $ξ_1, \ldots, ξ_n$ s.t. $\left \|\sum_{i=1}^n \mathbb{E} [ ξ_i] u_i u_i^* - \sum_{i=1}^n \varepsilon_i u_i u_i^* \right \| \leq 4 σ.$ A simple consequence of our result is an improvement of a Lyapunov-type theorem of Akemann and Weaver.

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