Exponential Scaling Barriers for Variational Quantum Eigensolvers

TL;DR

This paper demonstrates that the adaptive VQE algorithm's resource requirements grow exponentially with system size, challenging its scalability for large molecular simulations.

Contribution

It reveals that the number of adaptive iterations in VQE scales exponentially with system size, based on entropy predictions and extensive molecular benchmarks.

Findings

Number of adaptive VQE iterations correlates with Rénnyi entropy.

Exponential scaling of circuit depth with system size observed.

VQE unlikely to efficiently simulate large molecules without exponential resources.

Abstract

The Variational Quantum Eigensolver (VQE) is widely regarded as a promising algorithm for calculating ground states of quantum systems that are intractable for classical computers. This promise is typically motivated by the hope of mitigating the exponential growth of Hilbert space with system size. Here we scrutinize how the computational cost of adaptive VQE scales with the size of the target system. We demonstrate that the R\'enyi entropy derived from classical simulations predicts the required number of adaptive iterations of VQE with high accuracy ($R^2 \approx 0.99$). We validate this on a benchmarking set of more than 20 different molecules with active spaces ranging from four to ten orbitals. For these molecules, we find an exponential scaling of the number of adaptive iterations, and in turn, of the circuit depth with the system size. We therefore conclude that it is unlikely that VQE in its current form is able to simulate large molecular systems with high fidelity without exponential resource requirements.