An overview of QML with a concrete implementation in Haskell

Quantum Machine Learning using the ZXW-Calculus

The field of quantum machine learning (QML) explores how quantum computers can be used to more efficiently solve machine learning problems. As an application of hybrid quantum-classical algorithms, it promises a potential quantum advantages in the near term. In this thesis, we use the ZXW-calculus to diagrammatically analyse two key problems that QML applications face. First, we discuss algorithms to compute gradients on quantum hardware that are needed to perform gradient-based optimisation for QML. Concretely, we give new diagrammatic proofs of the common 2- and 4-term parameter shift rules …

量子机器学习 Playground

Quantum Machine Learning Playground

本文提出交互式 Web 可视化工具 Quantum Machine Learning Playground，融合量子计算与经典机器学习隐喻，以 data re-uploading 通用量子分类器为代表，降低 QML 学习门槛。

Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework

The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a possible application of quantum computing which may provide quantum advantage in the future. However, quantum versions of classical machine learning algorithms introduce a plethora of additional parameters and circuit variations that have their own intricacies in being tuned. In this work, we take the first steps towards Automated Quantum Machine Lea…

量子分类器的对抗鲁棒性保障

Adversarial Robustness Guarantees for Quantum Classifiers

本文证明量子分类器在特定条件下对数据扰动、局部攻击和通用对抗攻击具有鲁棒性，为量子机器学习的对抗鲁棒性提供了理论基础。