5 papers
Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
Emma Andrews, Nahyeon Kim, Prabhat Mishra
Quantum machine learning is a promising field for efficiently learning features of a dataset to perform a specified task, such as classification. Interval bound propagation (IBP) i…
Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
Sahan Sanjaya, Hari Krishna Parvatham, Emma Andrews +1
Quantum machine learning (QML) provides a promising framework for leveraging quantum-mechanical effects in learning tasks. However, its vulnerability to adversarial perturbations r…
Efficient Mutation Testing of Quantum Machine Learning Models
Emma Andrews, Prabhat Mishra
Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical c…
Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
Emma Andrews, Sahan Sanjaya, Prabhat Mishra
Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as by insertion of carefully craft…
Quantum Masked Autoencoders for Vision Learning
Emma Andrews, Prabhat Mishra
Classical autoencoders are widely used to learn features of input data. To improve the feature learning, classical masked autoencoders extend classical autoencoders to learn the fe…