A Comparative Analysis and Predictive Study of Deep Learning-Based Hybrid Quantum Error Mitigation Techniques for Noisy Intermediate-Scale Quantum Devices

Abstract

Quantum computing represents one of the most transformative technological developments of the twenty-first century. However, the practical realization of its potential on current Noisy Intermediate-Scale Quantum (NISQ) hardware remains severely constrained by noise and computational errors that arise from the inherent fragility of quantum states. This paper presents a systematic comparative analysis of three prominent quantum error mitigation techniques, partial quantum error correction (QEC), zero-noise extrapolation (ZNE), and deep learning-based noise prediction, and develops a predictive assessment of a proposed hybrid framework that integrates all three into a unified system. Through a thorough review of twelve peer-reviewed publications spanning 2017 to 2026, we establish what each technique has accomplished individually and to what degree of accuracy. We identify a critical and previously unaddressed gap: no existing work has systematically combined these three complementary approaches. Drawing on documented performance trends and theoretical complementarity, we predict that a well-designed hybrid framework could achieve classification accuracies of 85 to 90 percent on standard quantum machine learning benchmarks, compared to only 60 to 65 percent without error mitigation. These predictions are grounded in peer-reviewed experimental data. Our analysis provides a clear evidence-based roadmap for future experimental validation and establishes a strong foundation for the development of practical, resource-efficient quantum machine learning systems on near-term hardware.

CCS Concepts: Hardware → Quantum computation; • Computing methodologies → Machine learning; • Theory of computation → Quantum complexity theory