Noise-robust ground state energy estimates from deep quantum circuits

TL;DR

This paper demonstrates that the quantum computed moments (QCM) approach offers superior noise robustness over VQE for estimating ground state energies on noisy quantum hardware, especially with deep circuits.

Contribution

It introduces and experimentally validates the noise-filtering robustness of the QCM method for ground state energy estimation on quantum computers.

Findings

QCM maintains high error robustness where VQE fails.

QCM can handle circuits with up to ~500 CNOTs on 20 qubits.

VQE requires hardware error rates to improve by two orders of magnitude.

Abstract

In the lead up to fault tolerance, the utility of quantum computing will be determined by how adequately the effects of noise can be circumvented in quantum algorithms. Hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) have been designed for the short-term regime. However, as problems scale, VQE results are generally scrambled by noise on present-day hardware. While error mitigation techniques alleviate these issues to some extent, there is a pressing need to develop algorithmic approaches with higher robustness to noise. Here, we explore the robustness properties of the recently introduced quantum computed moments (QCM) approach to ground state energy problems, and show through an analytic example how the underlying energy estimate explicitly filters out incoherent noise. Motivated by this observation, we implement QCM for a model of quantum magnetism on IBM Quantum hardware to examine the noise-filtering effect with increasing circuit depth. We find that QCM maintains a remarkably high degree of error robustness where VQE completely fails. On instances of the quantum magnetism model up to 20 qubits for ultra-deep trial state circuits of up to ~500 CNOTs, QCM is still able to extract reasonable energy estimates. The observation is bolstered by an extensive set of experimental results. To match these results, VQE would need hardware improvement by some 2 orders of magnitude on error rates.