Adaptive Conformal Prediction for Quantum Machine Learning
This addresses the need for reliable uncertainty quantification in quantum machine learning, though it is incremental as it adapts an existing classical method to quantum hardware.
The paper tackled the problem of time-varying noise in quantum processors undermining uncertainty quantification guarantees in quantum conformal prediction, and introduced Adaptive Quantum Conformal Prediction (AQCP), which achieved target coverage levels and greater stability on an IBM quantum processor.
Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty quantification methods remain underdeveloped in the quantum domain, despite the critical need for reliable and trustworthy predictions. Recent work has introduced quantum conformal prediction, a framework that produces prediction sets that are guaranteed to contain the true outcome with user-specified probability. In this work, we formalise how the time-varying noise inherent in quantum processors can undermine conformal guarantees, even when calibration and test data are exchangeable. To address this challenge, we draw on Adaptive Conformal Inference, a method which maintains validity over time via repeated recalibration. We introduce Adaptive Quantum Conformal Prediction (AQCP), an algorithm which preserves asymptotic average coverage guarantees under arbitrary hardware noise conditions. Empirical studies on an IBM quantum processor demonstrate that AQCP achieves target coverage levels and exhibits greater stability than quantum conformal prediction.