Sample-efficient quantum error mitigation via classical learning surrogates

TL;DR

This paper introduces S-ZNE, a classical learning-based approach to quantum error mitigation that significantly reduces measurement overhead, enabling scalable and efficient zero-noise extrapolation for quantum circuits.

Contribution

The paper presents a novel surrogate-enabled ZNE method that performs error mitigation classically, reducing measurement costs from linear to constant for families of quantum circuits.

Findings

S-ZNE achieves comparable accuracy to traditional ZNE in practical scenarios.

Numerical experiments demonstrate effectiveness on 100-qubit quantum tasks.

Method offers scalable error mitigation with reduced measurement overhead.

Abstract

The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading solutions to improve computation fidelity with relatively low qubit-overhead, while full-scale quantum error correction remains a distant goal. However, QEM techniques incur substantial measurement overheads, especially when applied to families of quantum circuits parameterized by classical inputs. Focusing on zero-noise extrapolation (ZNE), a widely adopted QEM technique, here we devise the surrogate-enabled ZNE (S-ZNE), which leverages classical learning surrogates to perform ZNE entirely on the classical side. Unlike conventional ZNE, whose measurement cost scales linearly with the number of circuits, S-ZNE requires only constant measurement overhead for an entire family of quantum circuits, offering superior scalability. Theoretical analysis indicates that S-ZNE achieves accuracy comparable to conventional ZNE in many practical scenarios, and numerical experiments on up to 100-qubit ground-state energy and quantum metrology tasks confirm its effectiveness. Our approach provides a template that can be effectively extended to other quantum error mitigation protocols, opening a promising path toward scalable error mitigation.