Optimizing the depth of variational quantum algorithms is strongly QCMA-hard to approximate

TL;DR

This paper proves that approximating the optimal depth of variational quantum algorithms, including QAOA, is computationally intractable (QCMA-hard), highlighting fundamental limitations in optimizing near-term quantum algorithms.

Contribution

It establishes the first natural QCMA-hardness results for approximating the depth of VQAs, including QAOA, within certain multiplicative factors.

Findings

Approximating VQA depth is QCMA-hard for any constant >0.

Hardness persists even in simplified QAOA settings.

First natural problem shown to be QCMA-hard to approximate.

Abstract

Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimization Algorithm (QAOA) of [Farhi, Goldstone, Gutmann, 2014], have seen intense study towards near-term applications on quantum hardware. A crucial parameter for VQAs is the \emph{depth} of the variational ``ansatz'' used -- the smaller the depth, the more amenable the ansatz is to near-term quantum hardware in that it gives the circuit a chance to be fully executed before the system decoheres. In this work, we show that approximating the optimal depth for a given VQA ansatz is intractable. Formally, we show that for any constant $\epsilon>0$, it is QCMA-hard to approximate the optimal depth of a VQA ansatz within multiplicative factor $N^{1-\epsilon}$, for $N$ denoting the encoding size of the VQA instance. (Here, Quantum Classical Merlin-Arthur (QCMA) is a quantum generalization of NP.) We then show that this hardness persists in the even ``simpler'' QAOA-type settings. To our knowledge, this yields the first natural QCMA-hard-to-approximate problems.