2020/08/31 by Ji Guan, Wang Fang, Mingsheng Ying · 1 citation
Computer Science · Physics and Astronomy · #Artificial intelligence #Computer science #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum mechanics #Robustness (evolution) #Theoretical computer science #cs.LG #quant-ph
paper · pdf · doi:10.1007/978-3-030-81685-8_7
openalex publication_date 2021/01/01 · arxiv created 2021/05/31 · arxiv updated 2021/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract Several important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup to training classical classifiers and applications to data analytics in quantum physics that can be implemented on the near future quantum computers. However, quantum noise is a major obstacle to the practical implementation of quantum machine learning. In this work, we define a formal framework for the robustness verification and analysis of quantum machine learning algorithms against noises. A robust bound is derived and an algorithm is developed to check whether or not a quantum machine learning algorithm is robust with respect to quantum training data. In particular, this algorithm can find adversarial examples during checking. Our approach is implemented on Google’s TensorFlow Quantum and can verify the robustness of quantum machine learning algorithms with respect to a small disturbance of noises, derived from the surrounding environment. The effectiveness of our robust bound and algorithm is confirmed by the experimental results, including quantum bits classification as the “Hello World” example, quantum phase recognition and cluster excitation detection from real world intractable physical problems, and the classification of MNIST from the classical world.