提出可证明抗参数噪声的量子分类器训练方法，提升量子算法可靠性。
Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise
- 基于进化策略设计，无需修改主流优化算法即可抗参数噪声
- 在量子相变分类任务中验证了模型鲁棒性，无需额外调参
- 适用于多种量子线路，为实用化量子计算提供理论保障
量子计算的发展激发了人们对实现超越经典系统速度优势的期待。然而，噪声仍是实现可靠量子算法的主要障碍。本文提出一种可证明抗参数噪声的训练理论与算法，用于增强参数化量子电路分类器的鲁棒性。该方法与进化策略有自然联系，仅需对常用优化算法进行微小调整，即可保证对参数噪声的鲁棒性。该方法具有函数无关性，可适配多种量子电路结构，并在量子相变分类任务中成功验证。通过构建量子电路的可证明优化理论，本工作为近期量子计算机的实际、稳健应用开辟了新路径。
原文摘要 · Abstract (English)
Advancements in quantum computing have spurred significant interest in harnessing its potential for speedups over classical systems. However, noise remains a major obstacle to achieving reliable quantum algorithms. In this work, we present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers. Our method, with a natural connection to Evolutionary Strategies, guarantees resilience to parameter noise with minimal adjustments to commonly used optimization algorithms. Our approach is function-agnostic and adaptable to various quantum circuits, successfully demonstrated in quantum phase classification tasks. By developing provably guaranteed optimization theory with quantum circuits, our work opens new avenues for practical, robust applications of near-term quantum computers.
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