Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning

TL;DR

This paper introduces a probabilistic machine learning approach using Gaussian process regression to mitigate noise in variational quantum eigensolvers, improving accuracy and efficiency in quantum simulations.

Contribution

It presents a novel noise mitigation method employing parametric GPR with a custom prior based on the VQE ansatz, enhancing quantum computation accuracy.

Findings

Significant accuracy improvements in VQE outputs

Reduced number of QPU energy evaluations

Effective noise mitigation demonstrated on quantum models

Abstract

Quantum computing technology has the potential to revolutionize the simulation of materials and molecules in the near future. A primary challenge in achieving near-term quantum advantage is effectively mitigating the noise effects inherent in current quantum processing units (QPUs). This challenge is also decisive in the context of quantum-classical hybrid schemes employing variational quantum eigensolvers (VQEs) that have attracted significant interest in recent years. In this work, we present a novel method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations, focusing on VQEs. Our approach, grounded in probabilistic machine learning, exploits a custom prior based on the VQE ansatz to capture the underlying correlations between VQE outputs for different variational parameters, thereby enhancing both accuracy and efficiency. We demonstrate the effectiveness of our method on a 2-site Anderson impurity model and a 8-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit, showcasing substantial improvements in the accuracy of VQE outputs while reducing the number of direct QPU energy evaluations. This work contributes to the ongoing efforts in quantum error mitigation and optimization, bringing us a step closer to realizing the potential of quantum computing in quantum matter simulations.