解决量子机器学习的不确定性量化难题，提升预测可靠性。
Adaptive Conformal Prediction for Quantum Machine Learning
- 引入自适应校准机制，动态应对量子硬件噪声
- 在IBM量子处理器上实现目标置信水平且更稳定
- 适合关注量子模型可信度的研究者与工程师
量子机器学习旨在利用量子计算机超越经典机器学习算法。然而，当前量子领域仍缺乏可靠的不确定性量化方法，尽管这对生成可信预测至关重要。近期工作提出了量子共形预测框架，可生成具有用户指定概率覆盖真值的预测集。本文揭示了量子处理器固有的时变噪声会破坏共形保证，即使校准与测试数据满足可交换性。为此，我们借鉴自适应共形推断思想，提出自适应量子共形预测（AQCP），该算法在任意硬件噪声条件下仍能提供渐近平均覆盖保证。在IBM量子处理器上的实证研究表明，AQCP达到了目标覆盖率，且稳定性优于传统量子共形预测。
原文摘要 · Abstract (English)
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 a 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 provides asymptotic average coverage guarantees under arbitrary hardware noise conditions. Empirical studies on an IBM quantum processor demonstrate that AQCP achieves the target coverage level and exhibits greater stability than quantum conformal prediction.
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