Improvements in Quantum SDP-Solving with Applications

TL;DR

This paper advances quantum algorithms for semidefinite programming by developing improved Gibbs-samplers, leading to better bounds in various models and applications like shadow tomography, quantum state discrimination, and E-optimal design, while establishing fundamental lower bounds.

Contribution

It introduces improved quantum SDP-solvers with better bounds and applies them to multiple quantum information problems, also proving necessary lower bounds for quantum SDP-solving.

Findings

Enhanced bounds for quantum SDP-solving in different input models.

Improved algorithms for shadow tomography and quantum state discrimination.

Established lower bounds confirming the necessity of certain parameters.

Abstract

Following the first paper on quantum algorithms for SDP-solving by Brand\~ao and Svore in 2016, rapid developments has been made on quantum optimization algorithms. Recently Brand\~ao et al. improved the quantum SDP-solver in the so-called quantum state input model, where the input matrices of the SDP are given as purified mixed states. They also gave the first non-trivial application of quantum SDP-solving by obtaining a more efficient algorithm for the problem of shadow tomography (proposed by Aaronson in 2017). In this paper we improve on all previous quantum SDP-solvers. Mainly we construct better Gibbs-samplers for both input models, which directly gives better bounds for SDP-solving. For an SDP with $m$ constraints involving $n\times n$ matrices, our improvements yield an $\widetilde{\mathcal O}\left( \left( \sqrt{m} + \sqrt{n}\gamma \right)s \gamma^4\right)$ upper bound on SDP-solving in the sparse matrix input model and an $\widetilde{\mathcal O}\left( \left(\sqrt{m}+B^{2.5}\gamma^{3.5} \right)B\gamma^4 \right)$ upper bound in the quantum state input model. We then apply these results to the problem of shadow tomography to simultaneously improve the best known upper bounds on sample complexity due to Aaronson and complexity due Brandao et al. Furthermore, we apply our quantum SDP-solvers to the problems of quantum state discrimination and E-optimal design. In both cases we beat the classical lower bound in terms of some parameters, at the expense of heavy dependence on some other parameters. Finally we prove two lowers bounds for solving SDPs using quantum algorithms: (1) $\tilde{\Omega}(\sqrt{m}B/\eps)$ in the quantum state input model, and (2) $\tilde{\Omega}(\sqrt{m}\alpha/\eps)$ in the quantum operator input model. These lower bounds show that the $\sqrt{m}$ factor and the polynomial dependence on the parameters $B,\alpha$, and $1/\eps$ are necessary.