Real-time error mitigation for variational optimization on quantum hardware

TL;DR

This paper introduces a real-time error mitigation algorithm for variational quantum circuits, improving their trainability on noisy quantum hardware by reducing loss function corruption, demonstrated through simulations and experiments on single and multi-qubit systems.

Contribution

The paper presents a novel RTQEM algorithm that integrates error mitigation into VQC training, addressing noise-induced loss concentration not handled by existing methods.

Findings

RTQEM improves VQC trainability on noisy hardware.

Successful simulation and deployment on single-qubit devices.

Scalability demonstrated up to 8 qubits.

Abstract

In this work we put forward the inclusion of error mitigation routines in the process of training Variational Quantum Circuit (VQC) models. In detail, we define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional $\textit{u}$-Quark Parton Distribution Function (PDF) on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to 8 qubits.