QICS: Quantum Information Conic Solver

TL;DR

QICS is an open-source Python-based solver tailored for quantum information optimization problems, leveraging interior point methods, sparsity, and Hermitian matrix support, with demonstrated superior performance in quantum relative entropy tasks.

Contribution

This paper introduces QICS, a novel solver for quantum information problems that integrates advanced optimization techniques and outperforms existing quantum relative entropy solvers.

Findings

QICS outperforms state-of-the-art quantum relative entropy solvers.

QICS has comparable performance to leading semidefinite programming solvers.

The implementation details and numerical experiments validate QICS's efficiency.

Abstract

We introduce QICS (Quantum Information Conic Solver), an open-source primal-dual interior point solver fully implemented in Python, which is focused on solving optimization problems arising in quantum information theory. QICS has the ability to solve optimization problems involving the quantum relative entropy, noncommutative perspectives of operator convex functions, and related functions. It also includes an efficient semidefinite programming solver which exploits sparsity, as well as support for Hermitian matrices. QICS is also currently supported by the Python optimization modelling software PICOS. This paper aims to document the implementation details of the algorithm and cone oracles used in QICS, and serve as a reference guide for the software. Additionally, we showcase extensive numerical experiments which demonstrate that QICS outperforms state-of-the-art quantum relative entropy programming solvers, and has comparable performance to state-of-the-art semidefinite programming solvers.