Sample-efficient quantum error mitigation via classical learning surrogates

2025-11-10

AI Summary

To address the high measurement overhead and practical difficulty of implementing quantum error mitigation (QEM) on near-term quantum processors, this paper proposes a supervised zero-noise extrapolation (S-ZNE) method leveraging a classical learning agent. S-ZNE replaces the conventional multi-scale noisy circuit sampling in standard ZNE with a trained classical machine learning model, thereby reducing measurement overhead from linear to constant scaling and substantially lowering quantum resource requirements. The method operates entirely classically—performing noise extrapolation on parameterized quantum circuit families without additional quantum measurements. It is both scalable and broadly applicable across quantum algorithms. Theoretical analysis establishes that S-ZNE achieves accuracy comparable to conventional ZNE. Numerical experiments validate its effectiveness on ground-state energy estimation and quantum metrology tasks involving up to 100 qubits, demonstrating significant fidelity improvement without incurring extra quantum measurements.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Reducing quantum error mitigation measurement overhead
• Improving scalability for parameterized quantum circuits
• Achieving classical-side error mitigation via learning surrogates

Innovation

• Uses classical learning surrogates for error mitigation
• Performs zero-noise extrapolation entirely on classical computers
• Achieves constant measurement overhead for circuit families