Training variational quantum algorithms is NP-hard

TL;DR

This paper proves that training variational quantum algorithms involves solving NP-hard classical optimization problems, highlighting fundamental computational challenges and the presence of many local minima.

Contribution

It establishes the NP-hardness of classical optimization in variational quantum algorithms, even for simple systems, and discusses implications for training landscape complexity.

Findings

Classical optimization problems in variational quantum algorithms are NP-hard.

Hardness persists even for systems with logarithmically many qubits or free fermions.

Training landscapes contain many local minima, affecting convergence.

Abstract

Variational quantum algorithms are proposed to solve relevant computational problems on near term quantum devices. Popular versions are variational quantum eigensolvers and quantum ap- proximate optimization algorithms that solve ground state problems from quantum chemistry and binary optimization problems, respectively. They are based on the idea of using a classical computer to train a parameterized quantum circuit. We show that the corresponding classical optimization problems are NP-hard. Moreover, the hardness is robust in the sense that, for every polynomial time algorithm, there are instances for which the relative error resulting from the classical optimization problem can be arbitrarily large assuming P $\neq$ NP. Even for classically tractable systems composed of only logarithmically many qubits or free fermions, we show the optimization to be NP-hard. This elucidates that the classical optimization is intrinsically hard and does not merely inherit the hardness from the ground state problem. Our analysis shows that the training landscape can have many far from optimal persistent local minima. This means that gradient and higher order descent algorithms will generally converge to far from optimal solutions.