Uncertainty Quantification for Quantum Computing

TL;DR

This paper reviews how uncertainty quantification methods can be applied to quantum computing to understand noise, errors, and reliability, bridging applied mathematics and quantum information science.

Contribution

It introduces a rigorous mathematical framework for applying UQ techniques to quantum computing, emphasizing error analysis and algorithm validation.

Findings

Mathematical tools like probabilistic modeling and Bayesian inference are effective for quantum error analysis.

UQ methodologies can guide the development of scalable, reliable quantum algorithms.

Connecting UQ with quantum computing addresses key challenges in error mitigation and device characterization.

Abstract

This review is designed to introduce mathematicians and computational scientists to quantum computing (QC) through the lens of uncertainty quantification (UQ) by presenting a mathematically rigorous and accessible narrative for understanding how noise and intrinsic randomness shape quantum computational outcomes in the language of mathematics. By grounding quantum computation in statistical inference, we highlight how mathematical tools such as probabilistic modeling, stochastic analysis, Bayesian inference, and sensitivity analysis, can directly address error propagation and reliability challenges in today's quantum devices. We also connect these methods to key scientific priorities in the field, including scalable uncertainty-aware algorithms and characterization of correlated errors. The purpose is to narrow the conceptual divide between applied mathematics, scientific computing and quantum information sciences, demonstrating how mathematically rooted UQ methodologies can guide validation, error mitigation, and principled algorithm design for emerging quantum technologies, in order to address challenges and opportunities present in modern-day quantum high performance and fault-tolerant quantum computing paradigms.