---
title: "Sample-efficient quantum error mitigation via classical learning surrogates"
url: https://daily.dev/posts/sample-efficient-quantum-error-mitigation-via-classical-learning-surrogates-yrgbufqk0
source_url: https://www.nature.com/articles/s42005-026-02827-w
type: article
source: "Nature"
published: 2026-08-20T19:17:16.250Z
updated: 2026-08-20T19:17:35.623Z
tags: ["machine-learning", "quantum-computing"]
reading_time: 3
upvotes: 0
comments: 0
language: en
---

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# Sample-efficient quantum error mitigation via classical learning surrogates

**[Nature](https://daily.dev/sources/nature)** · 3 min read · 0 upvotes · 0 comments

## Summary

Researchers introduce surrogate-enabled zero-noise extrapolation (S-ZNE), a quantum error mitigation technique that uses classical learning surrogates to perform zero-noise extrapolation on the classical side rather than through repeated quantum measurements. Unlike conventional zero-noise extrapolation, whose measurement cost scales linearly with the number of circuits in a parameterized family, S-ZNE needs only constant measurement overhead regardless of how many circuits are involved. Theoretical analysis and numerical experiments on ground-state energy and quantum metrology tasks with up to 100 qubits show accuracy comparable to conventional zero-noise extrapolation, suggesting the approach could generalize to other quantum error mitigation protocols.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.nature.com/articles/s42005-026-02827-w>

## Questions this post answers

### What is surrogate-enabled zero-noise extrapolation (S-ZNE) in quantum error mitigation?

S-ZNE is a quantum error mitigation technique that performs zero-noise extrapolation entirely on the classical side using classical learning surrogates instead of repeated quantum measurements. While conventional zero-noise extrapolation has measurement cost scaling linearly with the number of circuits in a parameterized family, S-ZNE requires only constant measurement overhead for the entire family, and numerical experiments on up to 100-qubit ground-state energy and quantum metrology tasks confirm accuracy comparable to conventional zero-noise extrapolation.

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#quantum-computing](https://daily.dev/tags/quantum-computing)

[View this post on daily.dev](https://daily.dev/posts/sample-efficient-quantum-error-mitigation-via-classical-learning-surrogates-yrgbufqk0)