Error Mitigation Techniques for Robust Quantum Computing in Financial Modeling: Toward Reliable Quantum Advantage in Near-Term Financial Applications

Abstract

The acceleration of quantum computing (QC) presents unprecedented opportunities for computational finance, enabling efficient modeling of complex portfolios, derivative pricing, and risk optimization. However, the inherent noise, decoherence, and gate infidelity in near-term quantum devices termed Noisy Intermediate-Scale Quantum (NISQ) systems limit their practical deployment. This paper explores and benchmarks error mitigation techniques critical for achieving reliable quantum computation in financial modeling tasks. We review analytical and hybrid approaches including zero-noise extrapolation, probabilistic error cancellation, dynamical decoupling, and quantum subspace expansion and demonstrate their integration into quantum algorithms for Monte Carlo simulations and portfolio optimization. Building upon insights from Fatunmbi (2025) on the convergence of quantum computing and artificial intelligence, this study formulates a hybrid quantum-classical financial model resilient to stochastic noise. Empirical results show that quantum error mitigation (QEM) can reduce model variance and enhance accuracy by up to 35% under realistic noise conditions. The study concludes with policy and industry implications for the adoption of quantum-enhanced financial modeling systems.