Quantum Interior Point Methods for Semidefinite Optimization

Brandon Augustino1, Giacomo Nannicini2, Tamás Terlaky1, and Luis F. Zuluaga1

1Department of Industrial and Systems Engineering, Quantum Computing and Optimization Lab, Lehigh University
2Department of Industrial and Systems Engineering, University of Southern California

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Abstract

We present two quantum interior point methods for semidefinite optimization problems, building on recent advances in quantum linear system algorithms. The first scheme, more similar to a classical solution algorithm, computes an inexact search direction and is not guaranteed to explore only feasible points; the second scheme uses a nullspace representation of the Newton linear system to ensure feasibility even with inexact search directions. The second is a novel scheme that might seem impractical in the classical world, but it is well-suited for a hybrid quantum-classical setting. We show that both schemes converge to an optimal solution of the semidefinite optimization problem under standard assumptions. By comparing the theoretical performance of classical and quantum interior point methods with respect to various input parameters, we show that our second scheme obtains a speedup over classical algorithms in terms of the dimension of the problem $n$, but has worse dependence on other numerical parameters.

Semidefinite optimization (SDO) yields a fundamental family of convex optimization problems with vast expressive power. SDO problems generalize linear optimization problems, and aside from finding application in control, information theory, statistics and machine learning, SDO can also be used to approximate the solution to combinatorial optimization problems. The best performing classical algorithms for solving SDO problems are Interior Point Methods (IPMs), and therefore it is natural to investigate whether the IPM framework can be accelerated in a quantum setting. We propose two convergent quantum IPMs for SDO, obtaining a quantum speedup in the problem dimension at the cost of worse dependence on the precision and a condition number bound for the Newton linear systems that arise in each iteration.

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