Summary of Generating Universal Adversarial Perturbations For Quantum Classifiers, by Gautham Anil et al.
Generating Universal Adversarial Perturbations for Quantum Classifiers
by Gautham Anil, Vishnu Vinod, Apurva Narayan
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes QuGAP, a novel framework for generating Universal Adversarial Perturbations (UAPs) for quantum classifiers based on Parametrized Quantum Circuits (PQCs). The authors demonstrate the existence of additive UAPs for PQC-based classifiers and experimentally show that these attacks are effective. They also introduce a new method, QuGAP-U, for generating unitary UAPs using quantum generative models and fidelity constraints. The proposed framework achieves state-of-the-art misclassification rates while maintaining high fidelity between legitimate and adversarial samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Quantum Machine Learning (QML) is trying to use super-powerful computers called quantum computers to make machine learning better. But some bad guys have found a way to hack into these QML machines and make them do the wrong thing. The researchers in this paper came up with a new way to find these sneaky attacks and show that they work. |
Keywords
* Artificial intelligence * Machine learning
