Physics-inspired Machine Learning for Quantum Error Mitigation

TL;DR

This paper introduces NNAS, a physics-inspired neural network for quantum error mitigation that improves accuracy, reduces resource consumption, and requires less training data by leveraging quantum noise structure.

Contribution

The paper presents NNAS, a novel neural network architecture that incorporates quantum noise structure, enhancing interpretability and scalability in quantum error mitigation.

Findings

NNAS outperforms existing methods in error mitigation.

Reduces training data requirements by at least an order of magnitude.

Achieves over 50% error reduction in deep circuits.

Abstract

Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accuracy while reducing the sampling overheads of standard QEM methods. Yet, existing models lack physical interpretability and rely heavily on extensive datasets, hindering their scalability in large-scale quantum circuits. To tackle these issues, we introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for ML-QEM that incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. Experimental results demonstrate that NNAS outperforms current methods across a spectrum of metrics, including error mitigation capability, quantum resource consumption, and training dataset size. Notably, for deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors. NNAS also demands substantially fewer training data, reducing dataset reliance by at least an order of magnitude, due to its ability to rapidly capture noise accumulation patterns across circuit layers. This work pioneers the integration of quantum process-derived structural characteristics into neural network architectures, broadly enhancing QEM's performance and applicability, and establishes an integrative paradigm that extends to various quantum-inspired neural network architectures.