Abstract

Quantum error mitigation (QEM) is essential for reliable near-term quantum computing, yet practical deployment requires balancing mitigation strength against runtime overhead under nonstationary noise. This work introduces GSC-QEMit, a telemetry-driven context–forecast–bandit framework that adaptively selects mitigation intensity using unsupervised context discovery, uncertainty-aware Gaussian process forecasting, and a cost-aware contextual bandit. Evaluated on benchmark quantum circuits under drifting noise in simulation, the approach improves logical fidelity while reducing unnecessary intervention cost. Complementing this systems-level contribution, we apply unsupervised machine learning to broadband cryogenic transient dielectric spectroscopy data to analyze two-level system (TLS) noise mechanisms. Through clustering, dimensionality reduction, and spectral analysis, we recover invariant frequency structures and interference patterns directly from raw signals without explicit physical models. Together, these results demonstrate a unified role for machine learning in enabling practical quantum computing, both through adaptive control of system behavior and data-driven understanding of underlying physical noise processes."

Library of Congress Subject Headings

Quantum computing; Error-correcting codes (Information theory); Machine learning

Publication Date

7-10-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Data Science (MS)

Department, Program, or Center

Software Engineering, Department of

College

Golisano College of Computing and Information Sciences

Advisor

Daniel Krutz

Advisor/Committee Member

Travis Desell

Campus

RIT – Main Campus

Plan Codes

DSCI-MS

Download

Share

COinS